{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e09f8633",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from pandas.api.types import CategoricalDtype\n",
    "from plotnine import *\n",
    "from plotnine.data import mpg\n",
    "%matplotlib inline\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "'''from sklearn.metrics import r2_score'''\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "import pandas as pd\n",
    "from patsy import dmatrices\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d38a3bb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>estc_id</th>\n",
       "      <th>actor_id</th>\n",
       "      <th>actor_name_primary</th>\n",
       "      <th>publication_decade</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6234</th>\n",
       "      <td>T145345</td>\n",
       "      <td>176184097</td>\n",
       "      <td>Euclid.</td>\n",
       "      <td>1770.0</td>\n",
       "      <td>grc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4121</th>\n",
       "      <td>T136984</td>\n",
       "      <td>66462281</td>\n",
       "      <td>Plautus, Titus Maccius.</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>lat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>S111246</td>\n",
       "      <td>44299175</td>\n",
       "      <td>Galen.</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>grc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>R203879</td>\n",
       "      <td>25395604</td>\n",
       "      <td>Valerius Maximus.</td>\n",
       "      <td>1670.0</td>\n",
       "      <td>lat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4114</th>\n",
       "      <td>T153508</td>\n",
       "      <td>66462281</td>\n",
       "      <td>Plautus, Titus Maccius.</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>lat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5678</th>\n",
       "      <td>S116465</td>\n",
       "      <td>246763674</td>\n",
       "      <td>Claudianus, Claudius.</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>lat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2538</th>\n",
       "      <td>N11961</td>\n",
       "      <td>163408645</td>\n",
       "      <td>Longus.</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>grc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5522</th>\n",
       "      <td>T148777</td>\n",
       "      <td>100181283</td>\n",
       "      <td>Pindar.</td>\n",
       "      <td>1770.0</td>\n",
       "      <td>grc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1425</th>\n",
       "      <td>T107386</td>\n",
       "      <td>268955446</td>\n",
       "      <td>Plutarch.</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>grc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6376</th>\n",
       "      <td>T118863</td>\n",
       "      <td>281851250</td>\n",
       "      <td>Longinus, active 1st century.</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>grc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      estc_id   actor_id             actor_name_primary  publication_decade  \\\n",
       "6234  T145345  176184097                        Euclid.              1770.0   \n",
       "4121  T136984   66462281        Plautus, Titus Maccius.              1760.0   \n",
       "1296  S111246   44299175                         Galen.              1520.0   \n",
       "2025  R203879   25395604              Valerius Maximus.              1670.0   \n",
       "4114  T153508   66462281        Plautus, Titus Maccius.              1710.0   \n",
       "5678  S116465  246763674          Claudianus, Claudius.              1620.0   \n",
       "2538   N11961  163408645                        Longus.              1710.0   \n",
       "5522  T148777  100181283                        Pindar.              1770.0   \n",
       "1425  T107386  268955446                      Plutarch.              1790.0   \n",
       "6376  T118863  281851250  Longinus, active 1st century.              1750.0   \n",
       "\n",
       "     origin  \n",
       "6234    grc  \n",
       "4121    lat  \n",
       "1296    grc  \n",
       "2025    lat  \n",
       "4114    lat  \n",
       "5678    lat  \n",
       "2538    grc  \n",
       "5522    grc  \n",
       "1425    grc  \n",
       "6376    grc  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estc_ancient_works_authors=pd.read_csv('final_to_publish_enriched.csv')\n",
    "estc_ancient_works_authors.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86fcc29c",
   "metadata": {},
   "source": [
    "The following can't be replicated because the information on the publication year needs to be imported from estc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76f8df0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0140177\\AppData\\Local\\Temp\\ipykernel_34832\\1174126985.py:1: DtypeWarning: Columns (21,22,28,29,30,31,32,33,34,36,37,39) have mixed types. Specify dtype option on import or set low_memory=False.\n"
     ]
    }
   ],
   "source": [
    "estc_core=pd.read_csv(\"estc_full.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb8db276",
   "metadata": {},
   "outputs": [],
   "source": [
    "estc_classical_works_actor_id=pd.merge(estc_ancient_works_authors, estc_core[['estc_id','publication_decade', 'publication_year']], on='estc_id', how='left').reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "022371c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "ww3=estc_classical_works_actor_id[['estc_id','actor_id', 'actor_name_primary', 'publication_decade', 'publication_year']]\n",
    "ww3=ww3[ww3['publication_decade']<1800]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73bdb075",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_20_eebo_range=ww3[ww3['publication_decade']<1700]['actor_id'].value_counts()[0:20].index.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83ea088c",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_20_ecco_range=ww3[(ww3['publication_decade']>=1700) & (ww3['publication_decade']<1800)]['actor_id'].value_counts()[0:20].index.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a41395c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "most_common_authors=set(top_20_eebo_range+top_20_ecco_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "369262ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#If an author is in the 20 most frequent of the datasets, then it gets a \"frequent author\" label, otherwise not. \n",
    "#ww3['authors_variable']=np.where(ww3['actor_id'].isin(most_common_authors.index), 'Frequent_author', 'other')\n",
    "ww3['authors_variable']=np.where(ww3['actor_id'].isin(most_common_authors), 'Frequent_author', 'other')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49c4ef39",
   "metadata": {},
   "outputs": [],
   "source": [
    "ww3.to_csv('frequent_other_authors.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3bd8f93",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts_decade=ww3.pivot_table(index='authors_variable', \n",
    "                     columns='publication_decade', \n",
    "                     values='estc_id',\n",
    "                     fill_value=0, \n",
    "                     aggfunc='count').unstack().to_frame().rename(columns={0:'estc_id'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ae9cc02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>publication_decade</th>\n",
       "      <th>authors_variable</th>\n",
       "      <th>estc_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1470.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1470.0</td>\n",
       "      <td>other</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1480.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1480.0</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1490.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1770.0</td>\n",
       "      <td>other</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1780.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1780.0</td>\n",
       "      <td>other</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1790.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1790.0</td>\n",
       "      <td>other</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    publication_decade authors_variable  estc_id\n",
       "0               1470.0  Frequent_author        3\n",
       "1               1470.0            other        3\n",
       "2               1480.0  Frequent_author       11\n",
       "3               1480.0            other        5\n",
       "4               1490.0  Frequent_author        3\n",
       "..                 ...              ...      ...\n",
       "61              1770.0            other      141\n",
       "62              1780.0  Frequent_author      269\n",
       "63              1780.0            other      114\n",
       "64              1790.0  Frequent_author      335\n",
       "65              1790.0            other      191\n",
       "\n",
       "[66 rows x 3 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_decade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91f4c219",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, let's create a pivot table to sum the 'count' for each 'publication_year' and 'authors_variable'\n",
    "pivot_df = counts_decade.pivot_table(index='publication_decade', columns='authors_variable', values='estc_id', aggfunc='sum', fill_value=0)\n",
    "\n",
    "# Now, let's calculate the total ESTC IDs for each year\n",
    "pivot_df['Total_ESTC_IDs'] = pivot_df.sum(axis=1)\n",
    "\n",
    "# Calculate the percentage for Frequent and Non-Frequent authors\n",
    "pivot_df['Frequent_Authors_Percent'] = (pivot_df['Frequent_author'] / pivot_df['Total_ESTC_IDs']) * 100\n",
    "pivot_df['Non_Frequent_Authors_Percent'] = (pivot_df['other'] / pivot_df['Total_ESTC_IDs']) * 100\n",
    "\n",
    "# Reset index to make 'publication_year' a column again\n",
    "pivot_df.reset_index(inplace=True)\n",
    "\n",
    "# Select only the relevant columns to display\n",
    "result_df = pivot_df[['publication_decade', 'Frequent_Authors_Percent', 'Non_Frequent_Authors_Percent']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82f4f37a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XNlTVv/tdu3aRlZXFPffcU2ktOp2OsWPHsn//fi5fvkxBQQFbt27l9ttvN2Z81uaDDz6gZ8+eODk5Ge8zN23aZLy/NNwWtf1OKoqPj+f48ePG27TiGm+44QZSU1MbdZ8nhBCiMmkCLYQQwuizzz6jU6dO2NnZERgYWKlU5MKFCyiKQmBgYI3XjYyMrPR9TX1kMjIyKgVranLhwgVycnIqBVoqqtqbx9fXt9o+jo6OdSrn0Ol0jB49mvPnz/PSSy/RtWtXXF1d0el09O3b13iM7OzsWn/2qtvqeztVZDhPTbedodwlMzPzmj9XbVq3bl2nBrv29vaMGTOGMWPGGM85efJkfv75Z3799VduuOGGOp3P8LtOTExs8JobyhDAMNxuzz//PK6urqxcuZIPPvgAjUbD4MGDWbBgQZ1uk6CgoBq3HTlyxPh9YGAgU6ZM4cMPP+S5557j6NGjbN++nQ8//PCax4+Pj2fbtm3ceuutKIpiLE2bPHkyy5Yt49NPP2X+/Pl1+dHr5Vr/Pw35m6yth1R91ef2rOn/zfA7M6yvvvctNbGzs6t2m9V0nsbeVzZU1b97Q7mZoRSwJllZWajVarRa7TWnrr399ts8+eSTPPjgg7z66qv4+fmh0Wh46aWXKgWAMjMzr/o7MTCs76mnnuKpp56q8Zx1+b0IIYSoGwkACSGEMOrUqVOtL4b9/PxQqVRs3769xukyVbdVbYwM+l5Chp4stfHz88PX15f169fXeHnFbIXGio2N5ciRIyxfvpx77rnHuN3Q+8jA29sblUpVY/+eqk1N63s7VT2PWq029kOqyNDwtuLkH0vx9fXlscceY8uWLcTGxtY5ADRs2DDs7e1ZvXp1pbHx5lZYWMjvv/9OVFSU8QWtnZ0dTzzxBE888QQ5OTn8/vvvvPDCC4wZM4azZ89ec+pSTc1r09LSqgUDZs+ezeeff86aNWtYv349Xl5edZqg9+mnn6IoCt9//z3ff/99tctXrFjBa6+9hkajwcnJCaBac2VzvFBuyN9kTf/7DVXX2/Nq/5uG35Ep7lvKysrIzMys9Huv6TyNva9sqJ9++gnQN5c2rAXg3XffrXUCYGBgIFqtFo1Gc83755UrVzJ06FCWLFlSaXt+fn6l7319fdm3b1+169d0fwn6AO2kSZNqPGeHDh2uuiYhhBB1JyVgQggh6uTGG29EURRSUlLo1atXtY+uXbte8xjjxo3j5MmT/PHHH1c9T2ZmJlqttsbzNOTFgOEFV9WsIMMLr6ovyKpmGLi6utKrVy9Wr15tbIYKcOnSpWoTkxpzO7m6unL99dfz448/VlqrTqdj5cqVtG7dmvbt29fjJ6+f0tLSWjOMDO/uX6uRcUVBQUHcf//9/Pbbb3z22Wc17pOQkMBff/1V/8XWQqvV8sgjj5CZmcmzzz5b4z5eXl5MnjyZhx9+mKysrGpTsWry1VdfVSqvSU5OZteuXcYX2gbXXXcd/fv3Z8GCBXzxxRdMnz4dV1fXa655xYoVREVFsXnz5mofTz75JKmpqfz6668AxolvVW83w4v/imr7268ra/9N1vX2zM/Pr/bzf/nll6jVamO5mKnuW7744otq54ErQRdT3Fc2xJEjR5g3bx7h4eHcfvvtAAwYMAAvLy/i4uJqXEuvXr1wcHAwTrb77rvvrhpIVKlU1e4v//rrr2rT2oYNG1br76SiDh060K5dO44cOVLr+kwZ9BdCiJZOMoCEEELUyYABA/jnP//Jvffey4EDBxg8eDCurq6kpqayY8cOunbtauzZUZvHHnuMb775hokTJ/Lcc8/Rp08fCgsL2bp1KzfeeCPDhg3jjjvu4IsvvuCGG25g9uzZ9OnTB3t7e86dO8fmzZuZOHEit9xyS73WHh0dDcBHH32Eu7s7Tk5ORERE0LFjR6KionjuuedQFAUfHx/Wrl3Lxo0bqx3jlVdeYfz48YwZM4bZs2ej1Wp54403cHNzM07GMcXtNH/+fEaNGsWwYcN46qmncHBw4P333yc2NpavvvqqUdkCZ86cYc+ePdW2+/v7ExUVRW5uLuHh4dx2222MHDmS0NBQLl26xJYtW1i8eDGdOnWq9V362rz99tucPn2a6dOn89tvv3HLLbcQGBjIxYsX2bhxI8uWLePrr79u0Cj4CxcusGfPHhRFIT8/n9jYWD777DOOHDnC448/zsyZM437TpgwgejoaHr16oW/vz/JycksWrSIsLAw2rVrd81zpaenc8sttzBz5kxyc3OZM2cOTk5OPP/889X2nT17NlOmTEGlUtVpotOvv/7K+fPnWbBgQbWAEuj/ft977z2WLl1q7M01cuRI5s+fj7e3N2FhYWzatMk4Ga4iQ7BhwYIFjBs3Do1GQ7du3Wotg6qJOf8m66Iut6evry+zZs3izJkztG/fnl9++YWPP/6YWbNmGUsRTXHf4uDgwFtvvcWlS5fo3bs3u3bt4rXXXmPcuHEMHDgQMM195bX8+eefeHp6Ulpayvnz59m0aROff/45AQEBrF271vj7dXNz49133+Wee+4hKyuLyZMnExAQQEZGBkeOHCEjI8OYzfP2228zcOBArr/+ep577jnatm3LhQsX+Omnn/jwww+NveFeffVV5syZw5AhQzhx4gSvvPIKERERlSYK3n333bzzzjvcfffd/Pe//6Vdu3b88ssv/Pbbb9V+lg8//JBx48YxZswYpk+fTkhICFlZWRw7doyDBw82eAqeEEKIGli87bQQQgibU9MEntp8+umnyvXXX6+4uroqzs7OSlRUlHL33XcrBw4cMO4zZMgQpUuXLjVePzs7W5k9e7bSpk0bxd7eXgkICFDGjx+vHD9+3LhPaWmp8uabbyrdu3dXnJycFDc3N6Vjx47KAw88oJw6dcq4X03ToQznHzJkSKVtixYtUiIiIhSNRlNpCk1cXJwyatQoxd3dXfH29lZuu+025cyZMwqgzJkzp9IxVq1apXTt2lVxcHBQ2rRpo7z++uvKo48+qnh7ezfodqrN9u3bleHDhxuv27dvX2Xt2rWV9jHlFLC77rpLURRFKS4uVt58801l3LhxSps2bRRHR0fFyclJ6dSpk/LMM88omZmZNR6/tilgBmVlZcqKFSuU4cOHKz4+PoqdnZ3i7++vjBs3Tvnyyy+vOeWntilghg+1Wq14eHgoXbt2Vf75z38qu3fvrnaMt956S+nfv7/i5+dn/P3dd999SlJS0lXPbfjZPv/8c+XRRx9V/P39FUdHR2XQoEG1/i6Li4sVR0dHZezYsVc9tsHNN9+sODg4XHX60h133KHY2dkpaWlpiqIoSmpqqjJ58mTFx8dH8fT0VKZNm6YcOHCg2oSl4uJi5f7771f8/f0VlUqlAEpiYqKiKLVPUgsLC1PuueeeStvq8jdZn/uRqq523Wvdnob7my1btii9evVSHB0dleDgYOWFF16oNlmqrvctNbnnnnsUV1dX5a+//lKGDh2qODs7Kz4+PsqsWbOUS5cuVdu/sfeVNTFMATN8GH7W0aNHK4sXL6400a2irVu3KuPHj1d8fHwUe3t7JSQkRBk/fny1/9m4uDjltttuU3x9fY3/J9OnT1eKiooURdH/Lp566iklJCREcXJyUnr27KmsXr1aueeee5SwsLBKxzp37pxy6623Km5uboq7u7ty6623Krt27ar2N6ooinLkyBHl9ttvVwICAhR7e3slKChIGT58uHEinhBCCNNQKUoTGechhBBC2JjS0lJiYmIICQlhw4YN1l6OsBFr167lpptuYt26dXXulyRqd63bc+jQoVy8eJHY2FizrmP69Ol8//33XLp0yaznEUIIIcxFSsCEEEKIOrrvvvsYNWoUwcHBpKWl8cEHH3Ds2DEWL15s7aUJGxAXF0dycjJPPvkkMTExjBs3ztpLatLk9hRCCCFMSwJAQgghRB3l5+fz1FNPkZGRgb29PT179uSXX35h5MiR1l6asAEPPfQQO3fupGfPnqxYscLsvXGaO7k9hRBCCNOSEjAhhBBCCCGEEEKIZk7GwAshhBBCCCGEEEI0cxIAEkIIIYQQQgghhGjmJAAkhBBCCCGEEEII0cw1+ybQOp2O8+fP4+7uLs0DhRBCCCGEEEII0WwoikJ+fj6tWrVCrb56jk+zDwCdP3+e0NBQay9DCCGEEEIIIYQQwizOnj1L69atr7pPsw8Aubu7A/obw8PDw8qrEUIIIYQQQgghhDCNvLw8QkNDjbGPq2n2ASBD2ZeHh4cEgIQQQgghhBBCCNHs1KXljTSBFkIIIYQQQgghhGjmJAAkhBBCCCGEEEII0cxJAEgIIYQQQgghhBCimWv2PYDqQlEUysrK0Gq11l6KEDZFo9FgZ2dXp3pSIYQQQgghhBC2q8UHgEpKSkhNTaWgoMDaSxHCJrm4uBAcHIyDg4O1lyKEEEIIIYQQooFadABIp9ORmJiIRqOhVatWODg4SKaDEOUURaGkpISMjAwSExNp164darVUjQohhBBCCCFEU9SiA0AlJSXodDpCQ0NxcXGx9nKEsDnOzs7Y29uTnJxMSUkJTk5O1l6SEEIIIYQQQogGkLfzQbIahLgK+f8QQgghhBBCiKZPXtkJIYQQQgghhBBCNHMSABJCCCGEEEIIIYRo5iQAZAJancLuhEzWHE5hd0ImWp1i7SWJRtqyZQsqlYqcnBxrL0UIIYQQQgghhGg0CQA10vrYVAYu+IOpH+9h9teHmfrxHgYu+IP1salmO+f06dNRqVTVPuLj4812TksKDw9n0aJFdd5/3rx5aDQaXn/99Qadb+jQoTz22GMNuq4QQgghhBBCCNEUSACoEdbHpjJr5UFSc4sqbU/LLWLWyoNmDQKNHTuW1NTUSh8RERHV9ispKTHbGmzFsmXLeOaZZ/j000+tvZRrKi0ttfYShBBCCCGEEEK0QBIAqkJRFApKyq75kV9UypyfjlJTsZdh29yf4sgvKq3T8RSlfmVjjo6OBAUFVfrQaDQMHTqURx55hCeeeAI/Pz9GjRoFQFxcHDfccANubm4EBgbyj3/8g4sXLxqPd/nyZe6++27c3NwIDg7mrbfeqpYZo1KpWL16daV1eHl5sXz5cuP3KSkpTJkyBW9vb3x9fZk4cSJJSUnGy6dPn87NN9/Mm2++SXBwML6+vjz88MPGwMjQoUNJTk7m8ccfN2Y2Xc3WrVspLCzklVde4fLly2zbtq3S5YbzVfTYY48xdOhQ4+Vbt25l8eLFxvNVXO+ff/5Jr169cHFxoX///pw4caLSsZYsWUJUVBQODg506NCBzz//vNLlKpWKDz74gIkTJ+Lq6sprr71GdnY2d911F/7+/jg7O9OuXTuWLVt21Z9TCCGEEEIIIYRoDDtrL8DWFJZq6fyf3xp9HAVIyyui69wNddo/7pUxuDiY5texYsUKZs2axc6dO1EUhdTUVIYMGcLMmTN5++23KSws5Nlnn+X222/njz/+AODpp59m8+bNrFq1iqCgIF544QX+/PNPYmJi6nzegoIChg0bxqBBg9i2bRt2dna89tprjB07lr/++gsHBwcANm/eTHBwMJs3byY+Pp4pU6YQExPDzJkz+fHHH+nevTv//Oc/mTlz5jXPuXTpUqZOnYq9vT1Tp05l6dKlDB48uM5rXrx4MSdPniQ6OppXXnkFAH9/f2MQ6MUXX+Stt97C39+fBx98kBkzZrBz504AVq1axezZs1m0aBEjR47k559/5t5776V169YMGzbMeI45c+Ywf/583nnnHTQaDS+99BJxcXH8+uuv+Pn5ER8fT2FhYZ3XLIQQQgghhBBC1JcEgJqon3/+GTc3N+P348aN47vvvgOgbdu2LFy40HjZf/7zH3r27Mm8efOM2z799FNCQ0M5efIkrVq1YunSpXz22WfGjKEVK1bQunXreq3p66+/Rq1W88knnxgzd5YtW4aXlxdbtmxh9OjRAHh7e/Pee++h0Wjo2LEj48ePZ9OmTcycORMfHx80Gg3u7u4EBQVd9Xx5eXn88MMP7Nq1C4Bp06YxYMAA3n33XTw8POq0Zk9PTxwcHHBxcanxfP/9738ZMmQIAM899xzjx4+nqKgIJycn3nzzTaZPn85DDz0EwBNPPMGePXt48803KwWA7rzzTmbMmGH8/syZM/To0YNevXoB+p5HQgghhBBCCCGEOUkAqApnew1xr4y55n77ErOYvmz/Nfdbfm9v+kT41Om89TFs2DCWLFli/N7V1dX4tSGwYPDnn3+yefPmSgEjg4SEBAoLCykpKaFfv37G7T4+PnTo0KFea/rzzz+Jj4/H3d290vaioiISEhKM33fp0gWN5srPGxwczN9//12vcwF8+eWXREZG0r17dwBiYmKIjIzk66+/5p///Ge9j1eTbt26VVonQHp6Om3atOHYsWPVzjNgwAAWL15caVvV38esWbO49dZbOXjwIKNHj+bmm2+mf//+JlmvEEIIIYQQpqDVKexLzCI9v4gAdyf6RPigUV+9PYMQwrZJAKgKlUpVp1KsQe38CfZ0Ii23qMY+QCogyNOJQe38zXJH6erqStu2bWu9rCKdTseECRNYsGBBtX2Dg4M5depUnc6pUqmq9Sqq2NRYp9Nx3XXX8cUXX1S7rr+/v/Fre3v7asfV6XR1WkNFn376KUePHsXO7srvS6fTsXTpUmNgRq1WX3XN11JxrYaspoprrdqjSFGUatuq/j7GjRtHcnIy69at4/fff2fEiBE8/PDDvPnmm3VelxBCCCGEEOayPjaVl9fGVRp2E+zpxJwJnRkbHWzFlQkhGkOaQDeQRq1izoTOgD7YU5Hh+zkTOttElLxnz54cPXqU8PBw2rZtW+nDEEiyt7dnz549xutkZ2dz8uTJSsfx9/cnNfXKZLNTp05RUFBQ6TynTp0iICCg2nk8PT3rvF4HBwe0Wu1V9/n77785cOAAW7Zs4fDhw8aPbdu2sX//fmJjY2tcM8Dhw4frfb6adOrUiR07dlTatmvXLjp16nTN6/r7+zN9+nRWrlzJokWL+Oijj+p9fiGEEEIIIUzNmpOOhRDmJQGgRhgbHcySaT0J8nSqtD3I04kl03raTHT84YcfJisri6lTp7Jv3z5Onz7Nhg0bmDFjBlqtFjc3N+677z6efvppNm3aRGxsLNOnT0etrvznMXz4cN577z0OHjzIgQMHePDBBytlyNx11134+fkxceJEtm/fTmJiIlu3bmX27NmcO3euzusNDw9n27ZtpKSkVJpUVtHSpUvp06cPgwcPJjo62vgxcOBA+vXrx9KlS41rPnDgAJ999hmnTp1izpw5xuBQxfPt3buXpKQkLl68WOdspKeffprly5fzwQcfcOrUKd5++21+/PFHnnrqqate7z//+Q9r1qwhPj6eo0eP8vPPP9cpaCSEEEIIIYQ5aXUKL6+Nu+qk45fXxqHV1W+CsRDCNkgAqJHGRgez49nhfDWzL4vviOGrmX3Z8exwmwn+ALRq1YqdO3ei1WoZM2YM0dHRzJ49G09PT2OQ54033mDw4MHcdNNNjBw5koEDB3LddddVOs5bb71FaGgogwcP5s477+Spp57CxcXFeLmLiwvbtm2jTZs2TJo0iU6dOjFjxgwKCwvr3JQZ4JVXXiEpKYmoqKhKpWMGJSUlrFy5kltvvbXG6996662sXLmSkpISxowZw0svvcQzzzxD7969yc/P5+677660/1NPPYVGo6Fz5874+/tz5syZOq3z5ptvZvHixbzxxht06dKFDz/8kGXLlhlHzNfGwcGB559/nm7dujF48GA0Gg1ff/11nc4phBBCCCGEuexLzKqW+VORAqTmFrEvMctyixJCmIxKqdogpZnJy8vD09OT3NzcakGIoqIiEhMTiYiIwMnJqZYjtFxDhw4lJiaGRYsWWXspwork/0QIIYQQomVYcziF2V8fvuZ+i++IYWJMiPkXJIS4pqvFPKqSDCAhhBBCCCGEEAS41+3NvrruJ4SwLRIAEkIIIYQQQghBnwgfgj1rD+6o0E8D6xPhY7lFCSFMRsbAi1pt2bLF2ksQQgghhBBCWIhh0vGDKw/Wuo+tTDoWQtSfZAAJIYQQQgghhABgZKdAPJyq5wnYa1Q2NelYCFF/EgASQgghhBBCCAHAzoRM8orK8HK24/MZfXh1Yhc0aijVKgR7Olt7eUKIRpAAkBBCCCGEEEIIAFYdPAfATTEhDGrvzz/6hXNTd/3Er2U7E625NCFEI0kASAghhBBCCCEEl4rLWH80DYBJPVsbt88YEAHAur9TuZBXZJW1CSEaTwJAQgghhBBCCCFYH5tGUamOSD9Xurf2NG7v2tqTXmHelGoVVu5JtuIKhRCNIQEgIYQQQgghhBD8WF7+NalnCCpV5UlfMwbqs4C+2HuGolKtxdcmhGg8CQAJYaOGDh3KY489Zu1lCCGEEEKIFuB8TiG7T2cCMDEmpNrlozsHEuLlTNblEn46fN7SyxNCmIAEgBpj83zYurDmy7Yu1F9uBtOnT0elUvH6669X2r569epqkXpzSEpKQqVSVfuYNm2a2c9tCcuXL8fLy6vO+xcWFuLt7Y2Pjw+FhYX1Pt+WLVtQqVTk5OTU+7pCCCGEEEKYwurDKSgKXB/hQ6iPS7XL7TRq7u4XBsCnOxNRFMXSSxRCNJIEgBpDrYHN/60eBNq6UL9drTHbqZ2cnFiwYAHZ2dlmO8e1/P7776Smpho//ve//1XbR1EUysrKrLA6y/nhhx+Ijo6mc+fO/Pjjj9ZezlWVlpZaewlCCCGEEMLGKIrCjwdTALi1QvPnqu7o3QZnew3H0/KN2UJCiKZDAkBVKQqUXK7bR7+HYfDT+mDPH6/pt/3xmv77wU/rL6/rseoZQR85ciRBQUHMn3/1LKMffviBLl264OjoSHh4OG+99Valy8PDw5k3bx4zZszA3d2dNm3a8NFHH9VpDb6+vgQFBRk/PD09jdksv/32G7169cLR0ZHt27ejKAoLFy4kMjISZ2dnunfvzvfff1/peL/88gvt27fH2dmZYcOGsXz58kqZMXPnziUmJqbSdRYtWkR4eHilbcuWLaNTp044OTnRsWNH3n//feNlhuylH3/8kWHDhuHi4kL37t3ZvXs3oM/Guffee8nNzTVmNs2dO/eqt8PSpUuZNm0a06ZNY+nSpZUuM5zv8OHDxm05OTmoVCq2bNlCUlISw4YNA8Db2xuVSsX06dON++p0Op555hl8fHwICgqqtpYzZ84wceJE3Nzc8PDw4Pbbb+fChQvGyw232aeffkpkZCSOjo4oisL3339P165dcXZ2xtfXl5EjR3L58uWr/pxCCCGEEKJ5+jsll/j0SzjaqRnXNajW/Txd7Ln1On152Kc7kiy0OiGEqdhZewE2p7QA5rWq//W2vaH/qO37a3nhPDi41nl3jUbDvHnzuPPOO3n00Udp3bp6pP7PP//k9ttvZ+7cuUyZMoVdu3bx0EMP4evrWynI8NZbb/Hqq6/ywgsv8P333zNr1iwGDx5Mx44d677+Kp555hnefPNNIiMj8fLy4t///jc//vgjS5YsoV27dmzbto1p06bh7+/PkCFDOHv2LJMmTeLBBx9k1qxZHDhwgCeffLLe5/3444+ZM2cO7733Hj169ODQoUPMnDkTV1dX7rnnHuN+L774Im+++Sbt2rXjxRdfZOrUqcTHx9O/f38WLVrEf/7zH06cOAGAm5tbredLSEhg9+7d/PjjjyiKwmOPPcbp06eJjIys03pDQ0P54YcfuPXWWzlx4gQeHh44OzsbL1+xYgVPPPEEe/fuZffu3UyfPp0BAwYwatQoFEXh5ptvxtXVla1bt1JWVsZDDz3ElClT2LJli/EY8fHxfPvtt/zwww9oNBrS0tKYOnUqCxcu5JZbbiE/P98YpBNCCCGEEC2PIftndJcg3J3sr7rv9P4RrNxzhk3HL5CceZkw37q/hhFCWJcEgJqwW265hZiYGObMmVMt8wTg7bffZsSIEbz00ksAtG/fnri4ON54441KAaAbbriBhx56CIBnn32Wd955hy1btlwzANS/f3/U6itJZNu3bzd+/corrzBq1CgALl++zNtvv80ff/xBv379AIiMjGTHjh18+OGHDBkyhCVLlhAZGck777yDSqWiQ4cO/P333yxYsKBet8mrr77KW2+9xaRJkwCIiIggLi6ODz/8sFIA6KmnnmL8+PEAvPzyy3Tp0oX4+Hg6duyIp6cnKpWKoKDa3/0w+PTTTxk3bhze3t4AjB07lk8//ZTXXnutTuvVaDT4+PgAEBAQUK33ULdu3ZgzZw4A7dq147333mPTpk2MGjWK33//nb/++ovExERCQ0MB+Pzzz+nSpQv79++nd+/eAJSUlPD555/j7+8PwMGDBykrK2PSpEmEhenruLt27Vqn9QohhBBCiOalVKvjpyP6ps6TelZv/lxV2wA3hnbwZ8uJDJbvSmLOhC7mXqIQwkQkAFSVvYs+G6c+dryjz/bROIC2RF/+NfDx+p+3ARYsWMDw4cNrzJY5duwYEydOrLRtwIABLFq0CK1Wi0aj71HUrVs34+WGwEd6ejoA48aNMwZ2wsLCOHr0qHHfb775hk6dOhm/Dw0NNZZS9erVy7g9Li6OoqIiY0DIoKSkhB49ehjX2rdv30pNrA3BorrKyMjg7Nmz3HfffcycOdO4vaysDE9Pz0r7VvyZg4ODAUhPT69X1pNWq2XFihUsXrzYuG3atGk8/vjjvPzyy8bbtzEqrtOwVsPv5tixY4SGhhqDPwCdO3fGy8uLY8eOGQNAYWFhxuAPQPfu3RkxYgRdu3ZlzJgxjB49msmTJxuDWEIIIYQQouXYeiKDrMsl+Lk5MqitX52uc++ACLacyOC7A+d4YlT7a2YNCSFsgwSAqlKp6lWKxdaF+uDPsBdhyDNXGkBrHPTfm9ngwYMZM2YML7zwQqWsHtA3c6s6FaymMh97+8p32CqVCp1OB8Ann3xinGxVdb/Q0FDatm1b47pcXa/choZjrVu3jpCQyu8qODo61rquqtRqdbX9KjY1Npzn448/5vrrr6+0X9VgTMWfxXAbGa5fV7/99hspKSlMmTKl0natVsuGDRsYN26cMUOq4rrr04j5ar+bmn6/NW2v+LsA/W2xceNGdu3axYYNG3j33Xd58cUX2bt3LxEREXVemxBCCCGEaPpWHdKXf02MaYWdpm4tYge386NtgBvx6Zf47sA5ZgyU55BCNAXSBLoxDMEeQ/AH9J+HvVjzdDAzef3111m7di27du2qtL1z587s2LGj0rZdu3bRvn37OmenhISE0LZtW9q2bWssF6qvzp074+joyJkzZ4zHMnwYslc6d+7Mnj17Kl2v6vf+/v6kpaVVCqZUbK4cGBhISEgIp0+frnae+gQ2HBwc0Gq119xv6dKl3HHHHRw+fLjSx1133WUsyTNk3qSmpta4ZsP5gDqds6LOnTtz5swZzp49a9wWFxdHbm5upcysmqhUKgYMGMDLL7/MoUOHcHBwYNWqVfU6vxBCCCGEaNpyC0rZeEw/QKQu5V8GKpWK6f3DAVi+KwmtTnpJCtEUSAZQY+i0lYM/BobvdfV7Qd9QXbt25a677uLdd9+ttP3JJ5+kd+/evPrqq0yZMoXdu3fz3nvvVZqKZQnu7u489dRTPP744+h0OgYOHEheXh67du3Czc2Ne+65hwcffJC33nqLJ554ggceeIA///yT5cuXVzrO0KFDycjIYOHChUyePJn169fz66+/4uHhYdxn7ty5PProo3h4eDBu3DiKi4s5cOAA2dnZPPHEE3Vab3h4OJcuXWLTpk10794dFxcXXFwql+hlZGSwdu1afvrpJ6Kjoytdds899zB+/HgyMjLw9/enb9++vP7664SHh3Px4kX+/e9/V9o/LCwMlUrFzz//zA033ICzs/NVG08bjBw5km7dunHXXXexaNEiYxPoIUOGVCrBq2rv3r1s2rSJ0aNHExAQwN69e8nIyLhm0EgIIYQQQjQv6/5OpaRMR4dAdzoHe1z7ChVM6hnCG7+d4ExWAZuOXWB0l2v3zxS10+oU9iVmkZ5fRIC7E30ifNCoq2f7C9EYkgHUGMOer73Ma8gz+sst5NVXX61WHtWzZ0++/fZbvv76a6Kjo/nPf/7DK6+8Uq1UzFLr+89//sP8+fPp1KkTY8aMYe3atcbMnDZt2vDDDz+wdu1aunfvzgcffMC8efMqHaNTp068//77/O9//6N79+7s27ePp556qtI+999/P5988gnLly+na9euDBkyhOXLl9crA6h///48+OCDTJkyBX9/fxYurJ7J9dlnn+Hq6sqIESOqXTZs2DDc3d35/PPPAX2j6NLSUnr16sXs2bOrNYgOCQnh5Zdf5rnnniMwMJBHHnmkTutUqVSsXr0ab29vBg8ezMiRI4mMjOSbb7656vU8PDzYtm0bN9xwA+3bt+ff//43b731FuPGjavTeYUQQgghRPOw6tA5QB/Mqam1wNW4ONgxtU8bAJbtTDL10lqU9bGpDFzwB1M/3sPsrw8z9eM9DFzwB+tjU699ZSHqQaU089nPeXl5eHp6kpubWylTBKCoqIjExEQiIiJwcnKy0gpFbbZs2cKwYcPIzs6uNh1LWI78nwghhBBCND/JmZcZ8sYWVCrY/dwIgjzr/zzvfE4hgxZuRqtT+HX2IDrVM4tI6IM/s1YepOqLckM4bsm0noyNDrb0skQTcrWYR1WSASSEEEIIIYQQLYyh+fPAtn4NCv4AtPJyZmy0vvRr2c5Ek62tpdDqFF5eG1ct+AMYt728Nk56LAmTkQCQEEIIIYQQQrQgiqIYA0D1af5ckxkDwgFYffg8mZeKG7u0FmVfYhapuUW1Xq4AqblF7D2dadZ1aHUKuxMyWXM4hd0JmRJwasakCbSwWUOHDq3TeHghhBBCCCFE3R08k01yZgEuDhrGNLJ5c8823nRv7cmRc7l8ufcM/xrRzkSrbP7S82sP/lQ0belegj2daeXlRLCnM8GeTvoPL8PXzvi6OqBuQNPo9bGpvLw2rlIgKtjTiTkTOkvpWTMkASAhhBBCCCGEaEF+OKjP/hkbHYSLQ+NeEqpUKmYMjGD214f5bE8yDwyJwsFOCk3qIsC9bqV3OgVScgpJySkEsmvcx0GjJtDTsUKASB8wCvJwolV5oMjH1aFSs+/a+g+l5RYxa+VB6T/UDEkACCTLRIirkP8PIYQQQojmo7hMy89HzgNwa8/WJjnmuOhg/ut+jPT8Ytb9fZ5bepjmuM1dnwgfgj2dSMstqrEPkAoI8nTimwf6kZFfxPmcItJyizifW0hqThGpeUWk5hSScamYEq2Os1mFnM0qrPV8DnZqY/ZQkIcTG49dqLX/kAp9/6FRnYNkHH0z0qIDQPb29gAUFBTg7Oxs5dUIYZsKCgqAK/8vQgghhBCi6frjWDp5RWUEeTjRN9LXJMd0sFNzd78w3txwkmU7k7g5pv5j5VsijVrFnAmdmbXyYLXLDLfenAmdaePjQhsfF64Lq/k4pVodF/KKSM0t/8gpLP+60LgtI7+YkjIdyZkFJGcWXHNthv5D+xKz6Bdlmr8TYX0tOgCk0Wjw8vIiPT0dABcXF7mjEqKcoigUFBSQnp6Ol5cXGo3G2ksSQgghhBCN9GN58+ebe4SYNLNjap82/N8f8fx1Lpc/k7PpFe5jsmM3Z2Ojg5nWL4zPdydX2h5Ujz489ho1rb1daO3tUus+JWUVg0SFbDqWzk/lmWBXc+JCngSAmpEWHQACCArSNz0zBIGEEJV5eXkZ/0+EEEIIIUTTlXW5hM3H9a97Gjv9qypfN0duiQnhmwNnWbYzSQJA9XAiLR+AKb1D6R/lS4C7E30ifEwaoHOwUxPq40Kojz5IFODuVKcA0Nyf4th0LJ3be4UyuksgjnbypnBT1uIDQCqViuDgYAICAigtLbX2coSwKfb29pL5I4QQQgjRTKw9cp4ynUJ0iAftA91Nfvx7B4bzzYGz/BqbyrnsgqtmpAi9M5kF7EvMQqWCx0a2I9jTMq1JrtV/CPSNpUu0Orafusj2UxfxcrHn5pgQbuvVmi6tPC2yTmFaLT4AZKDRaOSFrhBCCCGEEKLZMpR/TTJTk+aOQR70j/JlV0Imn+9O5vkbOpnlPM3JDwfPATCwrZ/Fgj9Quf+QCioFgQx5R/83NYZOwR58/+c5vv/zHKm5RSzflcTyXUl0aeXBlN6h3NS9FV4uDhZbt2gcmc8nhBBCCCGEEM1cfPoljpzNQaNWcVNMK7OdZ8aACAC+2neGgpIys52nOdDpFGMAaPJ1lp+cNjY6mCXTehLkWXkcfZCnk3EEfJivK0+O7sCOZ4ezYkYfxncLxkGj5uj5PP6z5ih95m3iX18dYvupDHQ6mR5s6yQDSAghhBBCCCGauVWH9IGGIe398XNzNNt5hncMIMzXheTMAn44mMI/+tYyukqwNzGLc9mFuDvaMbqzdXpujo0OZlTnIPYlZpGeX1Rr/yGNWsWQ9v4Mae9P9uUSVh9O4Zv9Zzmels/aI+dZe+Q8IV7OTL6uNZOva23sNSRsi2QACSGEEEIIIUQzptMprD6kb/hr6ubPVanVKqb3Dwdg+c5EyQq5CkP2z/huwTg7WK8diUatol+ULxNjQugX5XvN5tPerg7cOyCCX2cP4ud/DeQffcPwcLIjJaeQxZtOMWjhZu76ZA9rDqdQVKq10E8h6kIygIQQQgjRrGh1yjXfyRRCiJZkb2IWKTn6TJORnQLNfr7J17XmrQ0nSci4zLZTGQztEGD2czY1l4vL+OXvVMA65V+moFKpiA7xJDrEkxfHd+K3o2l8d+AcO+IvsjM+k53xmbg72TExphVTerUhOsQDlary47E8ZluWBICEEEII0Wysj03l5bVxpOYWGbcFezoxZ0JnxkYHW3FlQghhPT9WyDRxsjd/pom7kz239wrl052JfLozSQJANfg1No2CEi3hvi5cF+Zt7eU0mpO9hokxIUyMCeFsVoGxcXRKTiEr95xh5Z4zdAxy5/ZeodzcIwQfVwd5zLYClaIozTonLy8vD09PT3Jzc/Hw8LD2coQQQghhJutjU5m18mC1cbaG9xENDS2FEKIlKSzR0vu/v3OpuIxvH+hHnwgfi5z3TGYBQ97cjKLA708Mpm2A6cfON2VTP9rD7tOZPDmqPf8a0c7ayzELnU5hV0Im3xw4y29H0ygp0wH68fLRIR4cPJNT7TrymF1/9Yl5WLUHUHh4OCqVqtrHww8/DICiKMydO5dWrVrh7OzM0KFDOXr0qDWXLMxt83zYurDmy7Yu1F8uhBBCVKHVKby8Nq5a8AeujLZ9eW0cWulFIYRoYTbEpXGpuIzW3s70smCmSRtfF2O52bKdSRY7b1NwNquA3aczUalgUhMt/6oLtVrFwHZ+vDu1B/teGMErE7sQHeJBiVZXY/AH5DHb3KwaANq/fz+pqanGj40bNwJw2223AbBw4ULefvtt3nvvPfbv309QUBCjRo0iPz/fmssW5qTWwOb/Vg8CbV2o3662XnM0IYQQtmtfYlalFPKqFCA1t4h9iVmWW5QQQtiAHw+mADCpRwhqC/dWMYyE//FgCjkFJRY9ty0z/E76R/kS4uVs5dVYhpeLA3f3C+fnfw1i/qToq+4rj9nmY9UAkL+/P0FBQcaPn3/+maioKIYMGYKiKCxatIgXX3yRSZMmER0dzYoVKygoKODLL7+05rKFOQ15Boa9WDkIZAj+DHtRf7kQQghRRXp+7cGfhuwnhBDNQXp+EdtPZQBwS0/LZ5r0jfShY5A7haVavt5/1uLnt0WKohinf91qhd+JLXBxqFsrYnnMNj2bGQNfUlLCypUrmTFjBiqVisTERNLS0hg9erRxH0dHR4YMGcKuXbtqPU5xcTF5eXmVPkQTM+QZGPqCPujzsrcEf4QQQlxTgLuTSfcTQojm4KfD59Ep0KONFxF+rhY/v0qlYsZAfRbQZ7uSKNPqLL4GW7M/KZszWQW4OmgYGx1k7eVYhTxmW4/NBIBWr15NTk4O06dPByAtLQ2AwMDKYwoDAwONl9Vk/vz5eHp6Gj9CQ0PNtmZhRr3v139WdKCxl+CPEEKIq+oT4UOwZ+1PFFXoJ4tYqvmpEELYgh8M5V9WzDS5qXsrfF0dOJ9bxG9HL1htHbbi+z/1mVDjuwXXOROmuTE8ZtdWkCiP2eZjMwGgpUuXMm7cOFq1alVpu0pV+c9CUZRq2yp6/vnnyc3NNX6cPSuphk3S1gVXvtaW1t4YWgghhAA0ahVzJnSu8TLDs4Y5EzqjsXD/CyGEsJZjqXkcS83DXqNiQjfrTVNystdw1/VtAPh0Z6LV1mELCkrK+OVvfTJDSy3/gsqP2bU9KstjtnnYRAAoOTmZ33//nfvvv9+4LShInw5XNdsnPT29WlZQRY6Ojnh4eFT6EE3M1oWw78Mr30cOrbkxtBBCCFHB2Ohgrgvzqrbd08VexskKIVqcVYf02T/DOwbg5eJg1bVM6xuGvUbFn8nZHDmbY9W1WNNvR/UT2dr4uNA7vGVnt4yNDmbJtJ4E1ZC9+8So9vKYbSY2EQBatmwZAQEBjB8/3rgtIiKCoKAg42Qw0PcJ2rp1K/3797fGMoUlGBo+R996ZZuLX/XG0EIIIUQVOp1C4sUCQP/O4ahOAQD0j/SVJ5JCiBZFq1NYfcj65V8GAR5O3NhNX+mxrAVnAX3/55Xmz5aeyGaLxkYHs+PZ4Xw1sy+L74hheEd/AA634CChuVk9AKTT6Vi2bBn33HMPdnZXaiBVKhWPPfYY8+bNY9WqVcTGxjJ9+nRcXFy48847rbhiYVY6rT7YEzH4yrYLR69MB9Nprbc2IYQQNu14Wj5Zl0twttdw1/Vh/GtEOwC2nMygqFQeP4QQLcfO+Iuk5xfj5WLPsA4B1l4OcGUk/Lq/U7mQ1/KmO6XkFLIrIROAST1DrLwa26FRq+gX5cvEmBD+Pb4zKhVsOp7OibR8ay+tWbJ6AOj333/nzJkzzJgxo9plzzzzDI899hgPPfQQvXr1IiUlhQ0bNuDu7m6FlQqLGPa8PthTkHllW+YpKCsuDwI9b721CSGEsGm7Ei4C+uaSDnZquoZ40srTiYISLTtOXbTy6oQQwnJ+LB8zPqFbKxzsrP6SD4CurT3pHe5NqVZh5Z5kay/H4lYdPIeiQN9IH0J9XKy9HJsU6e/GmM76VjAfbkuw8mqaJ6vfG4wePRpFUWjfvn21y1QqFXPnziU1NZWioiK2bt1KdHS0FVYpLK4g68rXujK4eNJ6axFCCNEk7IzXB3kGtPUF9M8jRnfRP5H87WjtE0SFEKI5uVRcZpy2ZWuZJveWZwF9sfdMi8rMVBTFOJFt8nUypfpqHhwaBcBPh89zPqfQyqtpfqweABKiRhUzgAAuxFlnHUIIIZqEUq2OfYn6Nw8GtPUzbh9THgD6/dgFyrQ6q6xNCCEsaX1sGoWlWiL8XIkJ9bL2cioZ3TmQEC9nsi6X8NPh89ZejsUcPJNN4sXLuDhoGBcdZO3l2LSYUC/6RvpQplNYuqPl9osyFwkAiVppdQq7EzJZcziF3QmZaHWK5U5uCAA5lJf7XYi13LmFEEI0OUfO5nC5RIuPqwOdgq5MAO0d7o23iz3ZBaXsS8q6yhGEEKJ5MJR/TeoRgkplW42G7TRq7u4XBuhHwiuKBV9fWJGh+fO46GBcHe2usbd4YIg+C+irfWfIKSix8mqaFwkAiRqtj01l4II/mPrxHmZ/fZipH+9h4II/WB+bapkFGAJAYeUT39IlA0gIIUTtdpSXf/WL9K00WcVOo2Zkp0AANpSXRAghRHN1PqeQ3af1z6Nv7mFb5V8Gd/Rug7O9huNp+ca1NmdFpVp+PqJ/DTX5OutPZGsKhrb3p2OQOwUlWj7f3fL6RZmTBIBENetjU5m18iCpuZW786flFjFr5UHLBIEMASDDNLALR81/TiGEEE3Wrnj940b/8v4/FY2NvtIHqKW82yyEaJlWH05BUfTN8G210bCni70xEPLpjiTrLsYCfjuaRn5xGSFezlwf4WPt5TQJKpWKB8uzgJbvSmpR/aLMTQJAohKtTuHltXHU9PTYsO3ltXHmLwczNIEOH6j/nJ9auTF0M2XVsjshhGiiCkrKOHQ2G4ABUX7VLh/Q1g9XBw2puUX8dS7X0ssTQgiLUBSFH8sbDd9qY82fq5o+IByATccvkJx52bqLMTND+det17WulKEqrm58t2BCvJzJvFzCd+W3oWg8CQCJSvYlZlXL/KlIAVJzi4yNNs2irASK8/Rfe4eBl75OuLlnAVm97E4IIZqofYlZlGoVQrycCfOt/o63k72GoR0DAJkGJoRovmJT8ohPv4SjnZpxXYOtvZyrivJ3Y2gHfxRFn+HRXKXmFhpLlG09KGdr7DVq7h+knxr38bbTMsjBRCQAJCpJz689+NOQ/RqksDy4pNKAoycEdtF/34wDQDZRdieEEE3UrgR9+deAtr61NjwdI+PghRDN3A/lzZ9HdQ7Ew8neyqu5NsNI+O8OnCO/qNTKqzGPVYfKS/LCfQjzdbX2cpqcKb1D8Xax50xWAevl8dskJAAkKglwdzLpfg1i6P/j4gNq9ZUAUHrzDADZTNmdEEI0UTvL312tOP69qmEd/HHQqEnIuEx8er6lliaEEBZRqtWx9oh+rPqtPZtGo+HB7fxoG+DGpeIyvjvQ/Ep8FEUxln9J8+eGcXGw4+5+4QB8sDVB+viZgASARCV9InwI9nSitupUFRDs6UQfczYwMwaAyht5BnTWf26mGUA2UXYnhBBNVNblEo6e15cN94uq3gDawN3JngHlDaJ/k2lgQohmZtvJDDIvl+Dn5sCgdrUHw22JSqXi3vJeQMt3JTW7NzsPnc3hdMZlnO013NDNtkvybNk9/cNxslcTm5LHzvjmPzXO3CQAJCrRqFXMmdC5xssMQaE5EzqjMWcDs6oBoMBo/ef0Y6BrfrWfNlF2J4QQTdTu8vKv9oFu18xONZSBrY+VNHIhRPNiaP58U/cQ7DRN5yXepB6t8XTWl/hsOta8gvM/lGf/jI0Ows3Rzsqrabp8XB2Y0isUgA+3JVh5NU1f07l3EBYzNjqYJdN64mRf+c8jyNOJJdN6MjbazBHsiiVgAD6RoHGE0gLITjTvua3AJsruhBCiidqZoC//6l/D9K+qRnYORK2Cv1NySckpNPfShBDCInILS9lYHjyZ1MQaDTs7aJjapw0Ay3YmWXcxJlRUquWn8pI8Kf9qvPsHRaJRq9h+6iKxKTLNszEkACRqNDY6mIgKjcr6Rvqw49nh5g/+wJVx74YMII0dBHTUf50eZ/7zW5hNlN0JIUQTtasO/X8M/Nwc6RWuvy/dIM0khRDNxC9/p1JSpqN9oBtdWnlYezn1dne/MDRqFbtPZ3IsNc/ayzGJjXEXyC8qo5WnE/0iay9PFnUT6uPC+PLJdh9slSygxpAAkKiRVqdw+uJl4/catcq8ZV8VXdY/mTcGgOBKGVgz7ANkE2V3QgjRBKXkFJKUWYBGreL6yLoFyaUMTAjR3PxYPv1rUs/WtU5CtGWtvJwZG62/b162s3lk+xsmst16XWvU8hzeJB4YEgnoA57JmZevsbeojQSARI1SsgspLrvSbyfrsgVHM1btAQQVGkHHWm4dFjQ2OphFd8RU226xsjshhGiCDNO/urX2rPPI49GdAwHYn5RF5qVis61NCCEs4UxmAfuTslGp4OaYplX+VdGM8pHwqw+f52ITv2++kFfEtpMZgD4oJ0yjSytPBrf3R6fAJ9ubR6DQGiQAJGoUn6EfkWtXHrHOvlxiuZPXFAAyjIK/0PxKwAw6BlVO2X18ZDvLld0JIUQTZBz/Xof+PwahPi5Eh3igU2DTsXRzLU0IISxi1SF98+cBUX4EeTbdfpE923jRvbUnJWU6vtx7xtrLaZRVh1LQKdArzJsIP9drX0HU2YPlWUDfHjjb5AOF1iIBIFGj+PRLAHRt7QlAVkEJimKh0YxXCwBlnYaS5pnyl1QlldHV0U7KvoQQohaKorCrfAJY/7b1668wpnN5GZj0ARJCNGGKovDjIUP5V9PN/gH9SPgZA/VZQJ/vSaakrGlO/lUUxTj9S5o/m16/SF+6tfakuEzHil1J1l5OkyQBIFGjUxf0AaA+5c0yS8p0FJRoLXNyYxPoCv0c3ALA1R9QIOO4ZdZhYUkXKweAzufI2HchhKjNqfRLZOQX42inpmcb73pdd0x5r4kdpy5yqbjMHMsTQgizO3gmh+TMAlwcNMb+Zk3ZuOhgAtwdycgvZt3f5629nAb561wup9Iv4Win5oZuksVvaiqVigeHRAHw2e5kLstjeL1JAEjUKD7jSgaQo53+zyTLUmVgNWUAQYU+QM2vETRAUmYBAN4u+j4WKTkF1lyOEELYNEP5V+9wH5zsNfW6brsANyL9XCnR6thyQsrAhBBNk6H589guQbg62ll5NY3nYKfm7n5hAHy6I5HdCRdZcziF3QmZaHUWqkRopO/Ls3/GRgfVuTedqJ8xXYII93Uht7CUr/eftfZymhwJAIlqFEUhvjwDqF2AOz6uDgBkF1ggAFRSAGWF+q9dqvR0ME4Ca559gAzd7PtF6QNfkgEkhBC12xmvf7OgLuPfq1KpVIyWaWBCiCasuEzLz3+lAs2r0fDUPm2wU6v4OyWPqR/vZfbXh5n68R4GLviD9bGp1l7eVRWXafnpiD5zScq/zEejVjFzsL4X0NLtpynVNs1yQWuRAJCoJj2/mPziMtQqCPdzwdtFHwCySAaQIftH4wgOVZqmBTbvSWCGErB+5c1Mz+cUWnM5Qghhs8q0OvaeNgSA6tf/x8Awcnjz8XSKSi1U4iyEECay+Xg6uYWlBHk4Gd88bA72J2VRVkO2T1puEbNWHrTpINCmY1d+J/3rMZxA1N+tPVvj5+bI+dwi1h5pmuWC1iIBIFGNof9PuK8rjnYay2YAVSz/UlVpgGycBHYULNWQ2kKKSrWcz9Vn/AwofxDPvFxCoaX6LgkhRBPyd0ou+cVleDjZ0aWVZ4OO0S3EkyAPJy6XaNmVcNHEKxRCNCdancLuhEybKkf64aB++tfEHq2azdAQrU7h5bU1Z/obbvGX18bZxO1fE0P516SeIc3md2KrnOw13DsgHIAPt5623LCiZkACQKKa+HT9CPioADcAvF0NGUCl5j95bf1/APw7gkoNhVlw6YL512JBZ7L0/X7cHe2I8HPFrbyO+3yuZAEJIURVhv4//aJ8G/wkW61WMbpLIAC/xTavxxQhhOmsj01l4II/mPrxHpspR8q6XMLm4/r+ZZN6NJ9So32JWaTm1t4CQQFSc4vYl5hluUXVUXp+EVtPZgBwq5R/WcS0vmG4Omg4cSGfzdLPr84kACSqOZVu6P+jDwD5lDclzrZICVgNE8AM7J3BR9/1vbmVgRnKv8L8XFCpVIR4OQOQki0BICGEqKox/X8qGlveB2jjsQs2+46yEMJ61semMmvlwWpBCWuXI/3813nKdApdWnnQIcjdKmswh/T8uvW/rOt+lrTm0Hm0OoUebbyI8nez9nJaBE9ne+68vg0AH2w5beXVNB0SABLVxJcHgNpWzQCydAlYTYxlYM2rEXRy+QSwcF9936NWXk6A9AESQoiqikq1/HkmG6DRPRb6RPjg5WJP1uUS9ifZ3jvKQgjrMZQj1RQatnY5kqH8qzk1fwYIcHcy6X6WoiiKsfxLmj9b1n0DI7HXqNiXlMWfydnWXk6TIAEgUU1CxpUJYAC+hgDQJVsKADWvUfCJ5RPArgSA9BlAEgASQojKDiRlU1KmI8jDiSh/12tf4SrsNGpGdCwvAzsq08CEEFfYajlSQsYljpzNQaNWcVP3VhY9t7n1ifAh2NOJqxX2Bns60SeihkoBKzp6Po8TF/JxsFNzY7fm9TuxdUGeTtwcEwLAh1sTrLyapkECQKKS7MslXCwP9EQF6J9Y22QGUHrzCgAZRsCH+boAEOKtDwCdkwCQEEJUsrO8YXP/tr6oqg4LaADDNLANRy9IE0khhFFdy4ze3xLPn8nZFrv/WFWe/TO4nR/+7o4WOaelaNQq5kzQT/2t7d79keFtba7BsiH7Z3TnQDyd7a28mpbngSH6kfAbj10wVrKI2kkASFQSX579E+LljIuDvhGxT/kYeMv0ALpGACigfBR8xgnQWqAptYUkXdSXgEX46YNuIZIBJIQQNdpV3gB6gIlG7A5q54eLg4aUnEJiU/JMckwhRNNX1zKj7acucuuSXQxauJk3fjvOyQv5ZluTTqew6lDzLP8yGBsdzJJpPQnyrHz725UHfT7fnUx+ke28Bigp07HmsP53IuVf1tE2wJ2RnQJRFPh4m/QCuhYJAIlKDCPgDf1/4EoGkGXHwNeS2ukVBg5uoC2BzOaR5qcfAa8P9IT5Vg4ApUgASAghjHILSvkrJRdofANoAyd7DUPa+wNSBiaEuKJPhA8BV8mwUQE+rg7cEtMKVwcN57IL+d/mBEa/s42xi7axZEuCyZ/H7U3MIiWnEHdHO0Z1DjTpsW3J2Ohgdjw7nK9m9mXxHTF8NbMvm54cgp+bI8fT8nnky0OUaXXWXiYAfxxPJ7uglEAPRwa187f2clqsWUP1WUCrDqVwIc/2moTbEgkAiUqqNoAG/YMbQHZBKTpzN7ozTgGrJQNIrYaATvqvm8kksHPZBSgKuDpo8HPT39aGHkBpuUUymUYIIcrtPp2JokCkv2u1d4cbw1AGJgEgIYSBWgW+5c/LqjIUIM27JZp37ujBgX+P4t2pPRjZKRB7jYrjafksWH+cAa//we0f7GblnmSyTJBJv+qQvtTohq7BONlrGn08W6ZRq+gX5cvEmBD6RfkS5uvK0nt64WSvZuvJDH2Dbhso2zWUf93cI8TmStNakuvCfOgV5k2JVsenOxKtvRybJgEgUcmpdH3aarsKASCv8jHwWp1CflGZeRdwrRIwaHaNoBPLy7/C/VyN/SwC3B3RqFWUahUuXiq25vKEEMJm7EowbfmXwbCOAdhrVJxKv2QchCCEaNm+3n+WY6n52KlV+FcJBAV5OrFkWk/GRgcD4OygYUL3VnxyTy/2vziS+ZO60jfSB5UK9iVl8e/VsfT57+/MWL6fNYdTKCip//PpwhItv/ytD1JP6hnS+B+wCeoe6sWiKTGoVPD5nmQ+3Zlk1fVcvFTMlhPpAExupiV5TcmDQ6IA+GLvGfJsqEzQ1thZewHCtiTUkAHkaKfBzdGOS8VlZBWU4OlipuZmilLHAFC0/nN68xgFn1xlAhjoJ9MEeTiRklPIuexCAj1sa9ylEEJYw05D/x8TlX8ZeDjZ0y/Kj20nM/jtaBoPDW1r0uMLIZqWc9kF/HfdMQCeG9eRewdEsC8xi/T8IgLc9VOoasv28HJxYGqfNkzt04bU3EJ+PpLK6sMpHD2fxx/H0/njeDrO9hpGdwlkYkwrBrXzx15T+3vyWp3CvsQsfo1N5VJxGSFeTvQOt60pWJY0NjqY58Z2ZP6vx3ltXRxtfFysVg635vB5ynQK3UO9aBfobpU1iCuGdwygXYAbp9Iv8cWeM8waGmXtJdkkyQASRpeKyzhfPu6yYgAIwNtVH/QxRfpqrYrzQFcera2tBxBcaQTdTDKAkgwBID+XStulEbQQQlyRlltEQsZl1CroF3mVNwkaaGyX8jKwWCkDE6IlUxSF5374m0vFZfQK8+beARHVypHqWuoT7OnMzMGRrHt0EL8/MZhHh7clzNeFwlItaw6fZ8byA/T57++8uOpv9iVmVWu1sD42lYEL/mDqx3v4bHcyALmFpWyIa9n3U/8cHMnUPm1QFHj0q0PElveGszRD+dfkFpqRZWvUahUPlGcBfbozkaJSrZVXZJskACSMDNk/fm6OeLlUTnW1yCQwQ/aPvSvYO9e+X2B5ACj3LBRZ5w7flAwTwMIqZADBlVHw0ghaCCGulH9Fh3iaJRN1VOdAVCo4ci6X1Fy53xWipfpy3xl2xF/E0U7NwsndTNbXpW2AO0+M7sCWp4ay+uEB3DsgHD83R7ILSvli7xlu/3A3gxZu5vVfj3MsNY9f/05l1sqDpOZWbmh7qVjLrJUHWR+bapJ1NUUqlYpXJnZhUDs/Cku13Ldiv8Xvt4+ez+VYah4OGjUTurey6LlF7W7q3opgTycy8ouNE/NEZRIAEkaGBtDtqmT/wJVJYFnmnAR2rQbQBs7e4FEeab/Q9MvAkmooAQNo5aUv+5IMICGEgB3l5V/9Tdz/x8Df3ZFeYd4AbDh6wSznEELYtrNZBcwrL/16ZmxHIv2rPyduLJVKRUyoF3MmdGHP88P5/L4+TL6uNW6OdqTkFPLB1gTGLd7Ov746xNVaHL+8Nq5FDwqx16j53109aR/oxoW8YmYsP8ClYjP3Kq3ghz/1wYVRnQOrvXEurMfBTs19AyMA/Uj4lvw/UhsJAAmjUzX0/zGwaAbQ1cq/DAyNoNObdhlYcZnWGOCpWgLWSkrAhBAC0Jdk7IrXP0YMaGv68i+DMeVlYOulDEyIFkenU3j2h7+4XKKld7g39/YPN/s57TRqBrXz583bunPg3yNZcldPxnYJwk6touwqL1wVIDW3iH2JWWZfoy3zcLJn6T298XNz4FhqHo9+ZZnx8CVlOlYf1geAbr1Oyr9szR192uDhZMfpi5fZ2MLLJWsiASBhVNMIeAPLZADVoQG0QTPpA3Q2qxCdAi4OGvzdHCtdZugBdC5bAkBCiJbt9MXLpOUV4aBR0yvMfM1PDQGgfUlZ5n3DQwhhc77Yd4ZdCZk42at5Y3J31BYe6e1kr2Fc12A++Md1vHZzdJ2uk55fdO2dmrlQHxc+vrsXjnZq/jiezmvlGVzmtOVEOlmXS/Bzc2RwO3+zn0/Uj5ujHf/oFwbAkq2nURTJAqpIAkDCKL6GEfAGPq4WzAByrUN6v2ESWBMvATNMAAvzvTIC3kCaQAshhN6u8vKv68K8cXbQmO08oT4udA72QKtT+P2YlIEJ0VKczSpg/i/6wMGzYzsS7ud6jWuYV9W+kLUJcJcpsQA92njzzpQYAJbvSmL5zkSznu+Hg/rmz5N6hmB3lQluwnqm94/AwU7NkbM57G3hmXJVyV+sAKCoVMuZLH0z4hozgMpLwLIul5pvEfXJAAqskAHUhKO6iRf1AaCIKuVfcKUELK+ojPwiM97uQghh43ZaoPzLwJAF9NtRSRsXoiXQ6RSe/v4IBSVa+kT4cE+/cGsviT4RPgR7OlFbDpIKCPbUj6MXejd0DebZsR0BeOXnOP44bp4gfualYjYdSwfg1p6tzXIO0Xj+7o7cdp3+9/PB1gQrr8a2SABIAPpAhE4BDyc7/N0dq13uUz4GPtsiJWB1eDDzbQdqeyjJh5wz5luTmSVn1jwBDMDV0Q6v8kk353MkxVcIUX9ancLuhEzWHE5hd0Jmk2yGqNUp7D6tf3zo39Y8DaArGhMdCMC2Uxe5bMGGokII61i5N5k9p7NwttfwxuRuFi/9qolGrWLOBP2bnVVXY/h+zoTOJptQ1lw8OCSSKb1C0SnwyJeHOHre9NOCfzpynjKdQtcQTzoEuZv8+MJ0Zg6KRK2CLScyOJaaZ+3l2AwJAAmgcv+fqqVIcCUDyLwlYHWcAgZg5wD+HfRfpzfdMjDDBLCIWlJ9W3lKGZgQomHWx6YycMEfTP14D7O/PszUj/cwcMEfTW508NHzueQWluLuaEe3EE+zn69DoDvhvi6UlOnYejLD7OcTQljPmcwC5v9yHIDnxnWsc+mVJYyNDmbJtJ4EeVYu8wrydGLJtJ6MjQ620spsl0ql4rVbohnQ1peCEi33LT9AWq5p30Q1lH9Nvk6yf2xduJ8r48r/Tz7adtrKq7EdEgASQMUR8DVHsn1srQk0VGgEHWue9VhAkrEHUPUSMIAQ7/JG0BIAEkLUw/rYVGatPEhqlSe+ablFzFp5sEkFgQzlX9dH+lik14JKpZJpYEK0ADqdwlPfH6GwVEvfSB/+0TfM2kuqZmx0MDueHc5XM/uy+I4YvprZlx3PDpfgz1XYa9S8f9d1tA1wIy2viPtW7DdZNuex1DxiU/Kw16i4qXsrkxxTmNeDQ6IAfebWuewCK6/GNkgASABXnwAGVwJAuYWl5huvWN8AkGEUfBNtBF1SpiMl2zACvuZ3nKQRtBCivrQ6hZfXxlFTsZdh28tr45pMOdiuBH0D6P5R5i//MhhdHgDafDyd4jKtxc4rhLCcz3YnsS8xCxcHDQtvtfzUr7rSqFX0i/JlYkwI/aJ8peyrDjyd7Vk2vTe+rg4cPZ/H7K8PmeQx74c/9dk/IzoGGickC9vWtbUnA9r6otUpfLLdvM3BmwoJAAmgQgAosOYAkKezPSqVvt9yTqGZGhI3OADUNEfBn8suQKeAs72GgBr6LsGVAFCKjIIXQtTRvsSsapk/FSlAam4R+5rAVIziMi37k/TrHNjOcgGgHqFeBLg7kl9cxq6ETIudVwhhGUkXL7Ng/QkAnh/XkTa1ZGKLpivUx4WP7u6Fg52a34+l899Gjocv1epYffg8IOVfTc0Dg/VZQN/sP2vediZNhASABGVaHacvlgeA/GsOANlp1Hg6lzeCNsc/jk4Lhdn6r+sbAMqMh9Km1yS5YvlXTX2X4MokMMkAEkLUVXp+3e4P67qfNR1MzqGoVIe/uyPtaslQNQe1WsXoLvpm0BtkGpgQzYpOp/DM939RWKqlX6Qvd11ve6VfwjSuC/Pm7du7A/DpzkQ+253U4GNtO5nBxUvF+Lo6MKSDv4lWKCxhUDs/urTyoLBUy2e7k629HKuTAJDgTFYBpVoFZ3uNMeOkJj7GUfBmCAAV5YJSXlrm7F2367gHg5MXKFq4eML0azKzpIv6OtTwqzQcbOWlb/wnASAhRF0FuDtde6d67GdNV8q/fGsNlJuLoQ/QxrgLTaZcTghxbct3JbEvqbz0y0amfgnzubFbK54eox8cM/eno2w+nt6g43xfXv51c48Q7C3Qj06Yjkql4oHyXkArdidRWNKyS7vlr1dwqrz8KyrA9aoPgoZaV7OMgjeUfzl5gsa+btdRqSAwWv91E+wDZMgAqq3/D1xpAp2WV0SpuXovCSGalT4RPgR71h7cUQHBnk70ifCx3KIaaEe8PgA0wIL9fwz6Rvri4WTHxUsl/JmcbfHzCyFML/HiZRb+pp/69cINnQj1kdKvluChoVFMvq51+Xj4g8Sdr99I8OzLJWw6pg8cSflX03RDdBChPs5kXS7h2wNnrb0cq5IAkLjS/6eW8i8Db2MGkBl6ANW3/49BYNOdBJaUacgAqv3Jh5+rIw4aNToFLuTZfrmGEML6NGoVk3qGXHWfORM623wj0fyiUv46lwtA/7b1fGwwAXuNmpGd9GVgv0kZmBBNnlan8PR3Rygq1TGgrS93Xd/G2ksSFqJSqZh3S1f6RfpyuUTLfSv21+t59dq/zlOi1dE52INOwR5mXKkwFzuNmpmDIgH4ePtp8w01agIkACSujIAPrHkEvIGPa3kPIHNkAF3Wv8tb/wBQ020EnVyHDCC1WkVweRmYNIIWQtRFYYmWNeWNKl0dNNUuf2hYVJMYIbz3dBZanUKYrwutva3zLv2YaH0Z2G9H01AUKQMToilbtjORA8nZuDpoWHBrN4uXlQrrcrBT88G064j0dyU1Vz8evqCkbuPhDdO/JPunabvtulB8XB04l13Iur9Trb0cq5EAkDAGgKKulQHkasYeQA3OACovAUtvWiVgpVod5wwj4K/SAwgqjILPlQCQEA2l1SnsTshkzeEUdidkNuueLv/3xynOZRfSytOJ3c+P4KuZfVl8RwwTuumDPntOZzWJYMbO8v4/A9pavvzLYHA7f5zs1ZzLLuRoPUsGhBC243TGJd74Td8v8sXxna0WVBbW5emiHw/v4+pAbEoes78+fM3nAycv5HPkXC52ahUTY1pZaKXCHJwdNEzvHw7AB1tPN4nnQuYgAaAWTqdTSMgwZABdPQBkaAJtlilgDQ0A+XfUf7504UoWURNwLrsQrU7ByV5d6wh4gyuTwKQETIiGWB+bysAFfzD14z3M/vowUz/ew8AFf7A+tvm9+3MiLZ+Pt50GYO5NXfBwtqdflC8TY0J46cbOONip+TM5u0mMgN8Vr39csEb/HwNnBw1D2uunvcg0MCGaJq1O4anvjlBcpmNQOz+m9gm19pKEFYX5uvLRP67DQaNmY9wF5v9y9fHwhuyfYR0D8HW7+nN2Yfv+0TcMZ3sNx1Lz2H6q6bx2NCUJALVw53MLKSjRYq9REXaNRnjGDCBzNoF2qWdTUkc38I7Qf92EysCMDaB9r954G65kAJ2TEjAh6m19bCqzVh4kNbdyADUtt4hZKw82qyCQTqfw4qq/KdMpjOocyOjyKVYGAR5O3Faevv7+lgRrLLHO0vOLOHEhH4B+UZbv/1PRWGMZ2AWrrkMI0TCf7kjk4Jkc3BzteF1KvwTQK9yHN27rBsAnOxJZuafm0eBlWh2rDqUAUv7VXHi7OnBHeRD4g622/VzIXCQA1MIZyr8i/Fyxu8ZIQ/NmAJW/G13fDCBokn2Aki7qA0BhV2kAbWAsAZNR8ELUi1an8PLaOGpK8DVse3ltXLMpB/v2wFkOJGfj4qDh5Zu61LjPA4OjUKtg68kMYlNyLbzCutudoH9ToHOwBz7lbz5Yy/AOgdipVZy4kE9i+X23EKJpiE+/xJsb9KVf/x7fyficSoiJMSE8Oao9AHN+OsrWkxnV9tkef5H0/GJ8XB0Y1iHA0ksUZnL/oEg0ahW7EjL561yOtZdjcRIAauGME8ACrl7+BZbKAGpEACi96QSAko0TwK7e/weulIClSABIiHrZezqzWuZPRQqQmlvUJMqhruXipWLm/6ofbfzEqPbG+42q2vi6MKG7vofBEhvOAtppGP9uhelfVXm62BuzkGQamBBNh1an8PT3+tKvwe39mdJbSr9EZY8Mb8utPVuj1Sk8/MVBjqdV7vX2fXn5103dW+FgJy+bm4sQL2duMj4Xim8xPSIN7Ky9AGFdVwJAV58ABhjfhc22pTHwAAGGUfBNJwBkeBf5ahPADEK8r2QAKYoiqctCXEVRqZad8RfZGHeBdX/VrbwrPb/p99eat+4YuYWldA72MDY4rM2soVGsOXyeX2JTOZ1xichrDACwNEVR2Fne/6e/FRtAVzSmSxDbT13kt6NpPDgkytrLEULUwSfbT3PoTA7ujna8PqmrPH8S1ahUKuZP6sq57AL2JmZx3/IDfD+rH0kXC0jOvMxvsfqgv5R/NT8PDIlk1aEUfo29wK+xV0q8gz2dmDOhc5OYltpQEsps4U7VIwPIUAJ2qbiM4jKtaRfSqAwgwySw46Az8brMxDACvi4lYMGe+jHwBSVacgvNEHwToonLvlzC93+e44HPD9DjlY3ct+IAX+8/S35x3ca7Brg7mXmF5rUr/iI/HkpBpYJ5k7pes5y3Y5AHIzsFoCi2Wf9+JquAlJxC7DUq+oTXsy+cmYzuHIhKBYfO5JB2lawyIYRtiE/P562NJwF46cbOtWZFCuFgp+bDf1xHpJ8rKTmFDF64makf7+G5H/U99ezUKs5mFVh7mcLEkmop6W6OPSKrkgBQC6YoypUMoDq8A+zuZIemvGFxToGJAxHGHkANeLfXJwLsnKGsELISTbsuM6g4Aj6iDhlATvYa/MqnDkgjaCH0zmQW8Mn200z5cDfXvbaRp747wm9HL1BYqiXY04m7+4Wx4t7eBHk4cbX3fIM8HOkTYRtBhoYoLtPy79WxgH6yRUyoV52uN2toWwBWHUohNde27lcM2T89Qr1xdbSNROUADyd6lN+2G+OkDEwIW1am1fHkd39RUqZjaAd/busl2Rvi6rxcHIzZs6XayiVAZTqFh75o3gGBlsbQI7ImzbFHZFUSAGrBMi4Vk1tYiloFkf7XDkSo1Sq8XewByDJlI2htKRSXNyOt7xQwALUGAsrHwV+INd26zCQlu5AynYKjnZrAOmYehHjp95NG0KKl0ukUjpzN4c3fTjDmnW0MfmMzr607xt7ELHQKdAr24NER7fj5XwPZ9dxwXpkYzZAOAcy9SV8iWlsQKMjTmWsM4rNpS7YkcPriZQLcHXlqTIc6X++6MG/6RvpQqlX4eJttBc53Juj7//S3gf4/FRmmga2XPkBC2LSPtydy5GwO7k52zJfSL1EHWp3CkmtkxDbngEBLsy8xq8X0iKyJbby1JqzCkP0T6uOCk72mTtfxdnHg4qUS004CM2T/qNTg5NWwYwR2gfOHID0OutxsqpWZRVKF8q9rjYA3aOXlzJFzudIIWjR5Wp3CvsQs0vOLCHB3ok+EjzGzsKriMi27EzLZGHeB349d4EJesfEyjVpfHjSqcyCjOgcS6lNzOeXY6GCWTOvJy2vjKj3Y+7k5kF1QwuGzOSzdkcj9gyJN+4NawOmMS7y/Wf+E9T8TOuPhZF+v6z80tC17Tu/jq31neGR4W6tP2wJ9oM8wAWyAjfT/MRjTJYh5vxxnz+kscgpK8HKx/u0lhKjs1IV83ikv/frPjZ0J9pTSL3Ft9QkIGIYCiKarrr0fm0OPyJpIAKgFSygPALWrQ/8fA7NMAjP0/3H2AXUDk9ICms4o+PpMADOQUfCiOVgfm1otEFO12V5uQSmbT6SzMe4CW09mcKlCHx9XBw1DOvgzqnMgwzoE1PkF+NjoYEZ1DqoWePpibzL/WXOU+b8ep3uoF71tpN9MXSiKwr9Xx1Ki1TGkvT/ju9a/WeGgdn50DfHk75Rclu9M5InRdc8gMpdjaXlkXS7BxUFD99Ze1l5OJWG+rnQMcud4Wj6bjqVzqzQFFcKm6Eu/jlCi1TG8Y4A07hV11tIDAi1NXXs/NvUekbWRAFALZmgAHVWPAJCvcRKYGQJADWkAbRDYdAJA9ZkAZtDKGACSBx7RNK2PTWXWyoNUTZ5Oyy3iwZUHub1Xa1JyCtl7OouyCinW/u6OxiyffpG+dc5WrEqjVlV71+4ffcM4kJTNT0fO8/AXB1n36CD83R0bdHxLW3UohV0JmTjaqXl1YnSDShxUKhUPDY1i1hcHWb4riZmDI3GvZxaRqe0q7//TJ8LHJkfujukSxPG0fNYfTZMAkBA25sNtp/nrXC7uTnbMu0VKv0TdtfSAQEvTJ8KHYE8n0nKLqj0vBX3bgCBPpybdI/JqbO/ZlbCYeGMG0LVHwBsYMoAybTUAlJ0IxZcavyYzMkwAq1cGUPko+HOSASSaIEOzvZoeZA3bvj1wjp3xmZTpFNoFuPHQ0ChWPdSfvc+PYN4tXRnWIaDBwZ/aGMa/tg1wIz2/mEe/OkSZVmfSc5hDTkEJ/113DIBHR7SjTR2mCdZmTJcgIv1dySsq48u9Z0y1xAYz9P8ZaGPlXwZjuuj7AG07mUFBSd2mzAkhzO9EWj6Lfz8FwNwJXQjylBfqou4MAYHaQoYq9BnLzTUg0NJo1CrmTKi5R6Th+zkTOtfaoqCpkwBQC1afEfAGhlHw5skAasSdqqsfuAXqv8443vg1mVGSsQSs7i/apARMNGXXqq03mHZ9G7Y8NZSNTwzhmbEd6dHGu859shrK1dGOD6b1xMVBw+7Tmbxd3jvClr3+63EyL5fQLsCNmY3sXaRWq5g1JAqAT3YkUlSqNcUSG6SkTGdsuNg/yjYDQJ2C3Wnj40JxmY5tJzOsvRwhBPrpqk+Vl36N6BjApJ4h1l6SaGJaekCgJTL0iKwaLA7ydGLJtJ7G1gTNkQSAWqjcglIy8vUNVesTALrSA8iEY+CNI+Ab2VQtQH/HbcuTwMq0Os5m6QNAYQ0oAcvIL7bqCzQhGqKuNfO9I3zqVRppKm0D3FlwazcA3t+SwO9xFyy+hrran5TF1/vPAjBvUleTlElNjAmhlacTGfnFfP/nuUYfr6GOnMuhoESLj6sDHYPqnplqSSqVijFd9G82rI+VaWBC2IIPtybwd0ouHk52zJOpX6KBWnJAoKUaGx3MjmeH89XMviy+I4avZvZlx7PDm/3vWnoAtVDxGfmAPp3RzbHufwY+rvr+EDbXAwj0ZWCnN8OFuMavyUzO5xRRplNwsFMT7FH39GRvF3uc7TUUlmpJyy2yyotkIRqqKdTWT+jeij+Ts1m+K4nHvz3Mun8NalRplTmUlOl4cdXfAEzpFWqyptUOdmr+OTiSuWvj+HBbAnf0DsVOY/n3h3bG68u/+kX5mj3zqzHGdAni4+2JbDqeTkmZziZ7FQnRUhxPy2PxJn3p18sTuxBYj+dWQlRV29AIyfxpvmrqEdncybOWFiq+AeVfoB8DD5BlkwGgaP1nG24EnWgYAe9T9xHwoH/XuZWX/kmNlIGJpqZPhM9V+zHYSm39Czd0okcbL/KLypj1xZ82l233yY7TnLxwCR9XB54b19Gkx57Suw2+rg6czSrk579STXrsujIEgAbYaPmXQc823vi7O5JfVMae05nWXo4QLVapVseT3x6hVKswslMgN8dI6ZdoPENAYGJMCP2ifCX4I5odCQC1UKcuNCwA5GOYAmbSMfD6J/2NDwBVKAFTamo3a32GBtBh9WgAbRDirc9GkEbQoqnRqFXcUssTc1uqrXewU/O/O3vi4+rA0fN5zP3JdoLJZ7MK+L/yd7n/Pb6TsRzXVJwdNNw7IByAJVsS0Oksex96ubiMQ2dyABjQ1rbfiVOrVYzqXF4GdlTKwISwliVbEjh6Pg9PZ3vm3dKwaYhCCNHSSACohYrPaHwGkGKqIIupMoD8OoBKA0U5kG+dd7CvJemivv9PhF/9S0tCJANINFE6ncKW8oa5ro6VJ3nZWm19Ky9nFt8Rg0oFX+8/y3cHzlp7SSiKwktrYikq1dE/ypdbepjnXe5/9AvHzdGOExfy+eN4ulnOUZt9SVmU6RRaezvTxse2Su9qYpgGtuHoBbQWDpYJ0VJpdQq7EzJZcziFr/ed4f826Zv2vzKxCwFS+iWEEHUiPYBaqIaMgIcrGUDFZToKS7W4OJjgT8hUTaDtncC3LVw8oS8D82jV+LWZWFIjMoBaeeobQadkSwBINC2/xqZxLDUPd0c7tjw9lJMXLtl0bf2gdv48PrI9b288yb9Xx9KllSedW3lYbT2//J3GlhMZOGjUvHqz+d7l9nS2Z1rfMD7YmsD/tsQzolOAxd5R31Wh/KspvIvfL9IXdyc7Ll4q5tCZbHqZqB+TEKJm62NTeXltXLWJkt1be3JTd9t7vieEELZKMoBaoIKSMs6VBxHqmwHk4qAxNrw0WR8gU4yBNwjsov9so32ADAGgiAY0cQ7xLh8FnysBINF0aHUKb288AcB9gyLwdXNsErX1jwxry9AO/hSX6Xjoiz/JKzLh5MN6yCsq5eW1+vuzWUOjiPKv3312fc0YGI6DnZpDZ3LYczrLrOeqaGe8/nGgv42Xfxk42KkZ0TEAgN+kDEwIs1ofm8qslQerBX8AjpzLlf9BIYSoBwkAtUCnM/RBCF9XB2NGT12pVCp8ysvAsi+b4AVRSQGU6suiGp0BBBX6ANleAKjSCPgGTBcyjII/n1O3kdpC2II1h1NIyLiMl4s9MwZGWHs5daZWq3jn9hhCvJxJyizgqW+PmK7stR7e+u0E6fnFRPi5MmtolNnPF+DuxJReoQC8vyXe7OcD/ZsJcal5APS38QbQFRnKwH47esEqfxtCtARancLLa+Oo7T9MBby8Nk5KMYUQoo4kANQCnUrXj4CPqmf2j4Gh+WiWKRpBF5a/w6y2B8f6laPVyDAJLN32RsGn5hZRqlVw0KgJLi/nqo+Q8gBQSk6hxRu0CtEQpVodi37XNy5+YHAUHk72Vl5R/Xi7OvD+XT1x0KjZEHeBj7eftuj5j5zN4bM9yQD89+ZonOw117iGafxzcCQatYrtpy7y97lcs59vV4K+/KtDoDv+7o5mP5+pDOngj6OdmjNZBRxLzbf2coRolvYlZtWY+WOgoH9+tS/RchmLQgjRlEkAqAW60v+nYQEgH1f9i7hsU5SAVWwAbYq+DwHlGUAZJ0BrnZKN2hjKv9r4ujSo7CXI0wmVCkrKdGSaqvxOCDP6/s9znMkqwM/NgXv6h1l7OQ3SPdSLlybo71cWrD/BXguN/S7T6nj+x79RFLilRwj921ouMybUx4WJ5T01LJEFZCj/GmDBn9EUXBzsGNzeH5AyMCHMJT2/blnPdd1PCCFaOgkAtUANHQFvUHESWKOZagKYgVcbcHAHXSlcPGWaY5pI0kV9ACi8AeVfAPYaNYHu+ikXKTIJTNi4olKtcWz5Q0PbmqZhvJVMu74NN8e0QqtTeOSrQxZ5obF8VxJxqfrxxi+O72T281X1YHm52fqjacY3DczFkAFk6+Pfa3KlDEwCQEKYQ4B73aZ71XU/IYRo6SQA1AI1dAS8gaFvULYpSsAME8BcTfTEX6Wy2UbQSZmG/j/1bwBtYGwELQEgYeO+3neG1NwigjycuPP6NtZeTqOoVCrmTepK+0A3MvKL+deXhyjT6sx2vvM5hby9UT/e+LlxHfFzs3xZVPtAd0Z1DkRR4IOtCWY7z7nsApIzC9CoVfSJaHqTtEZ2CkCjVnE8LZ/k8ixPIYTp9InwIdiz9uCOCgj2dGqS9x9CCGENEgBqYUrKdCSXByLqOwLewKYzgKBCI+hY0x3TBAwvDsIbMAHM4EojaAkACdtVWKLlvc36oMG/RrS1WO8ac3JxsGPJtOtwddCwNzGLNzecNNu55v50lIISLb3CvI0Nma3hofIsoNWHUsyWdbirvPyre2tP3JtYjygALxcH+kbqX3hKFpAQpqdRq3hpfOcaLzMU08+Z0NlmJ0oKIYStkQBQC5OUeRmtTsHN0Y5Aj4a9q2zaDCBzBIDKM4BsrBF0YiNLwOBKI+hz2RIAErZrxe4kLl4qJtTHmduus14Aw9Si/N1YOLk7oM+K2Rh3weTn2Bh3gQ1xF7BTq/jvLV1RW/FFTY823vSP8qVMp/DxNvM0wN5pLP9qWv1/KhpbYRqYEML0tOVT9qreGwZ5OrFkWk/GRgdbflFCCNFESQCohTH0cmgb4IaqgU2XjVPAbDUDKMD2SsC0OoWzWfqgTXhjSsC89GnQkgEkbFV+UamxZGj2iPY42DWvh5nx3YK5d0A4AE98e9ikZT+Xi8uYs0afuXj/oEg6BJlgMmIjPTS0LQBf7z/DxUvFJj22oijGBtBNafx7VaM66wNAfyZnk54njWiFMCWtTmHR7/qMy3+NaMtXM/uy+I4YvprZlx3PDpfgjxBC1FPzemYurqmxDaABfA0ZQJdNMGXLnCVgeSlQmG264zbC+ZxCSrQ67DUqYxlXQ7SqMApeCFv06Y4kcgpKifR35eaYVtZejlk8P64TPdt4kV9UxqyVBykq1ZrkuIt+P8n53CJaezsze0Q7kxyzsQa09aV7a0+KSnUs25lo0mOfvHCJi5eKcbJX0zPMy6THtqQgTydiQr0A2GCGrDAhWrKfjqSQkHEZT2d77h8USb8oXybGhNAvylfKvoQQogEkANTCGBpAN3QEPFToAWSrJWBOnuBZXnZywTbKwAx9l0J9GjYC3kCaQAtbllNQwifb9aVCT4xqj52meT7EONip+d9dPfFxdSAuNY85axqfbRh3Po9PdyYB8OrEaJwdbKNvkkqlYlZ5FtBnu5PJKzJB4L/cznh9+VfvcB8c7Wzj522osdEyDUwIUyvT6lj8u36a5D8HR+LRBPuECSGErWmez85FrU5dyAcalwFk7AF0uQSlvC67wQxTwFxMPL3BxvoAJZWXiUQ0ovwLrmQAZReUUlBS1uh1CWFKH207TX5xGR2D3LmhmaflB3s683939EClgm8OnOXbA2cbfCytTuGFVX+j1SmM7xrMsI4BJlxp443uHEjbADfyi8pYuSfZZMfd1Qz6/xgYxsHvTsgkt8B0QTIhWrIfD6aQlFmAr6sD0/uHW3s5QgjRLEgAqAXR6hROlzcibugEMAAvF/07MGU6hbyiRgYhzJEBBBBgW5PAkspv98aMgAfwcLLH3ckOkCwgYVsuXipmWXkGy5OjO1i1ebGlDGznxxMj2wPw0upYjp7PbdBxvtx3hsNnc3B3tOM/E2qedmNNarWKWUP0E8E+3ZFokpK3Mq2Ovaf1bwAMaML9fwwi/FxpH+hGmU7hjxNSBiZEY5WU6Vi8SZ/98+CQKFwd7ay8IiGEaB4kANSCnM0qoKRMh6Od2lhK1BBO9hpcy8sTshvTCFpRzBcAMmQA2UgJWFJ5CVi4X8MngBmEGPsASbNRYTuWbEmgsFRL99aejOxkWxks5vTwsLYM6+BPcZmOh744SG5h/bI/0vOLWLj+OABPjelAoIeTOZbZaDfFtCLEy5mLl0r4rhHZTgZ/peSSX1yGp7M9nVt5mGCF1meYBrY+VsrAhGisbw+cJSWnEH93R6b1DbP2coQQotmQAFALYpgAFunv1ujGecZJYI3pA1RyCbTl13c2dQlYtP5zehzodKY9dgMYSsAaMwHMwNgIWkbBCxuRllvE5+WlQU+O7tDgCYNNkVqt4p0pMYR4OZOcWcDT3x2pV2nsqz8fI7+ojG6tPW36RY69Rs0DQyIB+GDraUq1jbtf3XlKX/7VL7L5NHIdXR4A2noyg8IS0zQGF6IlKirV8t4f8QA8NDTKZnqiCSFEcyABoBbEFA2gDSr2AWqwy/oXANi7gEPjM2Mq8Y0CjYM+yJRjup4VDaHVKZwxZACZIABkyACSEjBhK97bfIqSMh19wn0Y1K7pl/PUl5eLA0um9cRBo2ZD3AU+2na6TtfbejKDtUfOo1bBvFu62nwg5PZeofi5OZCSU8jaI+cbdaydhv4/zejvpUsrD0K8nCkq1fHx9tOsOZzC7oRMtLpG9soTooX5at8Z0vKKCPZ0YmqfNtZejhBCNCsSAGpBTDEC3sA4CawxASBjA2gTl38BaOzBv4P+ays3gk7LK6owAr7x5R2tJAAkbMjZrAK+2a8vCXpydPsWlf1TUbfWXsb+PQt/O8He05lX3b+oVMtLq/U9yqb3jyA6xNPsa2wsJ3sN9w6IAOD9LQnoGhjYKCzRcjA5B4ABUWa4/7cSlUpFxyB9f723N55k9teHmfrxHgYu+IP1salWXp0QTUNhiZb3tyQA+hJbJ3vJ/hFCCFOSAFALYpYMoMaUgBn7/5i4/MsgwNAHqPEjmhvD0AA61NvFJGOxDf2bzkkASNiAxZtOUapVGNTOj+sjm8+L+Ya46/o23NIjBK1O4ZGvDpGeV3ufrnf/OMWZrAKCPZ14YnR7C66ycf7RLwx3Rzvi0y+x8VjDmh0fSM6iRKsj2NOJCL/GZ0XaivWxqWw6nl5te1puEbNWHpQgkBB1sHJPMhn5xbT2dub2XqHWXo4QQjQ7EgBqIRRFISHdHBlAjRh3a64G0AaBNhIAyjRMADNNmVtIeRaRZAAJa0vIuMSPB88B8MSophPEMBeVSsV/b4mmfaAbGfnFPPLVIcpq6JVz6kK+sUxszoQuuDWh6TYeTvb8o5++V9H7WxLq1e/IYGe8/r6/f5Rfs8kY0+oUXl5bc7ap4RZ6eW2clIMJcRWXi8tYslWf/fPo8HY42MnLFCGEMDW5Z20h0vKKuFRchp1a1ehR5AA+rvpR8I3qAWT2AJBhFLx1A0DJxglgpnmn21AClpZbJC8mhFUt+v0UOgVGdgqgRxtvay/HJrg42LFk2nW4OdqxLzGLNzacqHS5Tqfw4qpYSrUKIzsFMKZLoJVW2nAzBkbgaKfmyNkcdiVcvdStJrsM/X/aNp+MsX2JWaTm1p7xpQCpuUXsS8yy3KKEaGKW70oi63IJ4b4uTOoZYu3lCCFEsyQBoBbC0P8nzNfFJO+omGQKmNkDQOWTwLISoNR62TKJF003AQwgwN0JO7WKMp1Cer6MghfWcSw1z9gI+HHJ/qkkyt+NhZO7AfDh1tP8+ncquxMyWXM4hQXrj7MvKQtnew1zb+rSJDNg/NwcuaO3vjTj/S3x9bpubkEpf6fkAjCgbfNpAF3X+2K5zxaiZnlFpcbMyNkj25mkZF4IIUR1TSfvXDSKYQR8uwB3kxzPx8UEU8DMHQByC9SPly/Mgozj0KqHec5zDckmLgHTqFUEeTpxLruQ8zmFBHs6m+S4QtTHOxtPAjC+WzBdWtl+A2NLu6FrMPcNjGDpjkQe+uIgVXP1xnUNorW3iacfWtDMwZF8sfcMO+MzOXw2h5hQrzpdb/fpiyiKvhQ50KPxTfFtRYB73X6Wuu4nREvz6Y5EcgtLifJ35abukv0jhBDmIuH1FuKUCfv/gKkzgMzUBFqlqtAHyDqTwHQ6xVgCZspmp4YysHPZ0gdIWN5f53LYEHcBtQoeH9nO2suxWT3aeAFUC/4ArDqY0qSbArf2dmFijP5F2vub654FZOj/05ymfwH0ifAh2NOJ2vK5VECwpxN9Isz0eCdEE5ZTUMLS7YkAPDayPRp108uMFEKIpsLqAaCUlBSmTZuGr68vLi4uxMTE8OeffxovVxSFuXPn0qpVK5ydnRk6dChHj1q3p0tTZMoG0FBhCphJxsCbsQzAUAZmpT5AaXlFFJfpsFOrCPEyXaZOa+MoeCknEJb31gZ99s/NPUJoa6KswuZGq1P477pjV92nqTcFnjU0EpUKNsRd4NSF/DpdZ2d5/5/+zaj8C/SZmXMm6PvO1fTSVQHmTOgsL2yFqMHH20+TX1xGxyB3xncNtvZyhBCiWbNqACg7O5sBAwZgb2/Pr7/+SlxcHG+99RZeXl7GfRYuXMjbb7/Ne++9x/79+wkKCmLUqFHk59ftyabQM4yAN1kGUHkJWE5hacNfwJi7BAwqNIKONd85rsIwASzUxzQj4A0MGUApOQUmO6YQdbE/KYutJzOwU6uYPUKyf2rTEpoCtw1wZ3RnfRPrJVsSrrl/Wm4RpzMuo1ZB38jmlQEEMDY6mCXTehLkWb3My8PJjn6RzSvoJYQpZF0uYdnOJECf/aOWIKkQQpiVVXsALViwgNDQUJYtW2bcFh4ebvxaURQWLVrEiy++yKRJkwBYsWIFgYGBfPnllzzwwAOWXnKTlHmpmKzLJahU+uakpuDlop8CpiiQW1hqzAiqF4sEgMpLwNKtUwKWdFEfoDFV/x+DEG/JABKWpygKb/6mn2p1W69Qk0wUbK5aSlPgh4a25bejF1hz5DyPj2pPqE/t93U74/XZP11DPPF0trfUEi1qbHQwozoHsS8xi/T8Irxc7Jn701ESLxbw2ro43ritu7WXKIRN+XBrAgUlWqJDPJrkVEQhhGhqrJoB9NNPP9GrVy9uu+02AgIC6NGjBx9//LHx8sTERNLS0hg9erRxm6OjI0OGDGHXrl01HrO4uJi8vLxKHy2doQF0a29nnB00JjmmvUZtfAKf1ZAyMJ1O35wZzBsA8u8EqOByBlxKN995amFoAG2qCWAGrYwlYNIDSFjOroRM9iZm4aBR86/hba29HJvWUpoCdw/1YmBbP7Q6xTjBpzbNtfyrKo1aRb8oXybGhDCkfQBvTO6OSgXf/XmObSczrL08IWxGen4RK3YnAfDEqPZNciqiEEI0NVYNAJ0+fZolS5bQrl07fvvtNx588EEeffRRPvvsMwDS0tIACAys/I5AYGCg8bKq5s+fj6enp/EjNDTUvD9EE2BsAG2i7B8DYx+ghjSCLsoBRaf/2lxNoAEcXMAnUv+1FfoAJRkDQCbOAPLSv2hMkSbQwkIUReHNDfrsnzuvb2MMQoqataSmwA8NiwLg2wNnycgvrnEfRVGMGUADm3kAqKpe4T7c0y8cgOd//JvLxWXWXZAQNmLJlgSKSnXEhHoxrEOAtZcjhBAtglUDQDqdjp49ezJv3jx69OjBAw88wMyZM1myZEml/aq+I6AoSq3vEjz//PPk5uYaP86ePWu29TcVxhHwgaZt1urt0ogMIEMDaEdP0Ji5FMDYB8gKASBDCZgJJ4DBlQyg/OIy8opKTXpsIWryx/F0Dp3JwclebXzBL2p3tabAhu+bS1PgfpG+xIR6UVym49OdiTXuk5BxmQt5xTjYqbkuzNvCK7S+p8d0oLW3Myk5hSxcf9zayxHC6lJzC/li7xkAnhwt2T9CCGEpVg0ABQcH07lz50rbOnXqxJkz+geEoKAggGrZPunp6dWyggwcHR3x8PCo9NHSxZs5A6hhASAzj4CvyDAJzMJ9gHQ6heQs85SAuTjYGQNwkgUkzE2nU4yTv+7pH97ky5YspbamwEGeTiyZ1pOx0c1j2o1KpeKhofqg4Oe7k8ktrB6U3lVe/tUrzBsne9OUIjclro52zJ/UFYAVu5PZn9R0m38LYQr/2xxPSZmOPuE+LS4rUAghrMmqAaABAwZw4sSJSttOnjxJWFgYABEREQQFBbFx40bj5SUlJWzdupX+/ftbdK1NmTEAFGjaAJBhEljjAkAWmAQTYJ1JYBfyiygq1aFRq2jtbfpymSuNoCUAJMxr/dE04lLzcHO048HBkv1TH2Ojg9nx7HC+mtmXxXfE8NXMvux4dnizCf4YjOwUSPtANy4Vl7FyT3K1yw3lXwNa8Au9Qe38ub1XawCe/f4vikq1Vl6RENZxLruAb/brM/Qfl94/QghhUVYNAD3++OPs2bOHefPmER8fz5dffslHH33Eww8/DOjfVXzssceYN28eq1atIjY2lunTp+Pi4sKdd95pzaU3GXlFpaTl6afMmGoEvIGxB5CtB4AMk8AyToDWcr0XDOVfrb2dsTfhCHiDVp4SABLmp9UpvL1Rn/0zY2AE3g2Z+NfCVWwK3C/Kt1mUfVWlVquYVZ4FtHRHIoUlV4IbWp3C7gT9fX7/qOY3/r0+XhzfmQB3R05fvMyi309ZezlCWMW7m+Ip1Sr0j/KlXwu/TxBCCEuzagCod+/erFq1iq+++oro6GheffVVFi1axF133WXc55lnnuGxxx7joYceolevXqSkpLBhwwbc3U3bz6a5SijP/glwd8TDybS9dgwvBLMa0gTakgEg7wiwd4GyIsi6+pQaUzLXBDADQx+gcxIAEmb005EU4tMv4elsz/2DIqy9HGHDJnRrRWtvZ7Iul/DN/jPG7UfP55JXVIa7ox1dQzytuELr83S257Wb9WXJH28/zd/ncq28IiEsKznzMt8fPAfoe/8IIYSwLKsGgABuvPFG/v77b4qKijh27BgzZ86sdLlKpWLu3LmkpqZSVFTE1q1biY6OttJqm54rDaBNm/0D4OPSmAwgfTmARXoAqdUQ0En/tQXLwBLNNAHMoLWxBKzILMcXolSrM2YpPDAk0uRBZNG82GnUPDBEnwX00bbTlJTpJz3uKC//6hvli50ZsiGbmtFdgrixWzBancLT3x8x3k5CtASLN51Cq1MY0t6f68Ka/hREIYRoauSZWDNnrgbQUDEDqAFTqAxTwCyRAQRXysAs2Ag62TABzMwZQCnZBWY5vhA//HmO5MwC/NwcmN4/3NrLEU3Abde1xs/NkfO5Raw5nALArnh9xucAKfUwevmmLni72HM8LZ8PtiZYezlCWER8+iVWH9LfLzwxSrJ/hBDCGiQA1MxdaQBt+pI5H1d9NoDN9wACCCgPAFlwFHxSeQZQhIlHwBuEeEkGkDCf4jIt/7dJn/0za2hbXBzsrLwi0RQ42WuMpYLvb4ln64l09pzW39/3jZQAkIGvmyNzb9I/Lr37xylOXsi38opEU2PorbXmcAq7EzLR6hRrL+maFm86hU7RN43vHupl7eUIIUSLJAGgZu6UOTOAGlUCZuEAUKBlA0CKopCcacgAMk8JmCED6EJ+EaVaKSEQpvX1vrOczy0iyMOJu65vY+3liCbkruvb4GyvJvFiAfcs209Z+QvT6cv3sz421cqrsx03dW/FiI4BlGoVnvn+rybxAl7YhvWxqQxc8AdTP97D7K8PM/XjPQxc8IdN/3+dSMvn57/OA5L9I4QQ1iQBoGasqFTL2fLyILP0ACovAcsvLqt/DwNrBYBykqHY/O+0pucXU1iqLR8Bb54AkK+rAw52ahQF0nIlC0iYTmGJlvc2xwPwyPC2ONlrrLwi0ZTsjL9IYWn1x4QLuUXMWnnQpl+kWpJKpeK/t3TF3dGOw2dzWLYz0dpLEk3A+thUZq08SGqVx/00G///emfjSRQFbugaROdWHtZejhBCtFgSAGrGEjIuoSjg5WKPrxlGN3s42WOYZpxT30lglg4AufiAe7D+6/RjZj9d4kV9+VeIlzMOdub5N1OrVcYysBSZBCZM6LPdSWTkF9Pa25nbe4VaezmiCdHqFF5eW3OvNUN+y8tr4yTbpVyQpxMvjNcPKXhzwwmSyh87hKiJ4f+rpv8eW/7/ik3JZf3RNFQqeGykZP8IIYQ1SQCoGTNOAAtwQ6VSmfz4arXKWAZWr1Hw2lIoKh99a6kAEEBAZ/1nC0wCM4yAN1f5l0ErLycAUrIlACRMI7+o1NiUdvaIdmYLYIrmaV9iVrXMhIoUIDW3iH2JWZZblI27o3co/aN8KSrV8dyPf6GzsRfvwnY01f+vdzaeBGBCt1a0N0NPSiGEEHUnz+ybMWMD6ADTl38ZGCeB1acPUGF2+RcqcPYy+ZpqZewDZP5JYEnl/X/M1QDa4EojaAkACdNYtjOJ7IJSIv1cuaVHiLWXI5qY9Py6laPWdb+WQKVS8fqkbjjba9hzOouv9p+x9pKEjWqK/1+Hz+aw6Xg6ahXMHtnO2ssRQogWTwJAzZghABRlhgbQBj7GRtD1GAVvKP9y9ga1BXuLBEbrP1ugEbQhjd9cI+ANDI2gz+dKAEg0Xk5BCR9vOw3AY6PaY6eRhwhRPwHuTibdr6Vo4+vCU2M6ADD/l+MS1Bc1aor/X2+XZ//c0qO1WZ+PCiGEqBt5dt+MGUvAzJhu610+Cr5eJWCGAJCrnxlWdBWBhhKwo6CYN8XekAEUbvYSMH0A6JyUgAkT+Hj7afKLy+gQ6M6NXYOtvRzRBPWJ8CHY04naio5VQLCnE30ifCy5rCZhev9werbx4lJxGS+u+hvFzI9TounpE+GDk/3Vn7qrgMvF9XhTzowOJGWx7WQGdmoVs0dI9o8QQtgCCQA1U6VanbERsTlLwAyTwOo1Ct7SDaAN/NqD2g6KcyEvxWyn0Y+A19/24WYuAWstJWDCRC5eKmbZziQAnhjdHrXa9H3DRPOnUauYM0EfbK/6F2T4fs6Ezmjk76sajVrFwsndcNCo2Xwig9WHzfc4JZqmn/86T1ENE/YqUoD7P/uTBeuPU6at54RWE3trgz7757ZerWlj5jfEhBBC1I0EgJqp5MwCynQKrg4aWnmaLxXYpyE9gKwVALJzBN/yd6DMWAaWkV9MQYkWtQpCzTQC3qBVhSlg8m6xaIwPtiRQUKKlW2tPRncOtPZyRBM2NjqYJdN6ElTlsSfI04kl03oyNlqyy2rTNsDd2Cfl5bVxZOQXW3lFwlak5hby0mr9EIvxXYMJrvL/FezpxLtTezC9fzgAS7YkcOcne7mQZ51+QLsSLrL7dCb2GhWPDJfsHyGEsBV21l6AMI/49HwAosw0AczAMAUsuyElYC5WKAEI7AIZx/QBoPZjzHIKQ/lXiLf5RsAbBJdPASsq1ZFdUGoMyAlRH2m5RXy+JxmAJ0d3MOt9hmgZxkYHM6pzEPsSs0jPLyLAXV/2JZk/1/bPwZGs+yuVuNQ85v50lP/d1dPaSxJWptMpPPP9X+QVldGttSeL7ohBrVLV+P81oXsreof78OwPf7EvMYsbFm9n8R09GNjOcmX3iqLwdnn2zx292xgHVgghhLA+yQBqpiwxAQwamgFUPp7U0hlAULkPkJkYGkCHm7kBNICjnQZ/d0dAysBEw/1vczzFZTp6h3sz2IIvEkTzplGr6Bfly8SYEPpF+Urwp47sNWoWTu6GRq1i3d+prI9Ns/aShJV9vieZ7acu4min5u3bY7DXqK/6/zW+WzA/PTKAjkHuZF4u4R+f7mXx76fQ6iyTKbzt1EUOJGfjYKfm4WFtLXJOIYQQdSMBoGbqlIUCQIYx8A3LALJGAKh8Eli6+UbBJ2UaJoBZpt5dGkGLxjibVcDX5WOnJftHCNsQHeLJg0MiAXhpTSy5BbbR1FdYXnz6Jeb9cgyAF27oVOfndZH+bqx+eAB39A5FUeCd308yfdk+Mi+Zt6xQn/1zAoBp14dVKwUVQghhXRIAaqaME8ACzDcBDK6Mgc+61FQCQF30ny+ehLJ6rLkeko0TwMyfAQTSCFo0zrt/nKJUqzCwrR99I63wPymEqNG/hrcjyt+VjPxiXl1nvjcthO0q1ep44tvDFJfpGNTOj3/0DavX9Z3sNbx+azfevK07TvZqtp+6yPj/2/H/7N13mFTl2cDh38xs7w1YWOouTQRBREAEFhRLNMYSY9QYzZdYYu8Yo0aNLZLEFo1dE1s0JhqDGiuwdFCaIH1Z2lK29z4z3x/vnJlZ2DIze2bOmdnnvi6ueZl2XhH2zDznKXyzuyJIO4YFW0vYsL+a+Ggb187KC9pxhBBCBEYCQBHI4XBSWBriErBwyQBKyYHYVHC0qSBQEBSFsAQMYICrD1CxBICEj+wOJysKy3l58S7+tWY/oCZ/CSHMIy7axrwLx2OxwL/W7Kdge6nRWxIh9uyCnXy3v5qUuCj+eOH4gKczXnjCQD66fjp5fRI5VNPExS+t5KXFhboPj3A6nTzxpfpsdfm0Ie4SdSGEEOYhAaAIVFzVSFOrgxiblUHpwW28p5WANbU6aGyx+/YiIwNAFosnCygIfYDaj4APTQlYjmQACT98tukg0x9fwCUvr+SRT7fgcEJslJUSgybFCCE6d8KQdPdUp99+sJG65jZjNyRCZv2+Kp5duBOAh88f1+NSqlHZyfz3hun8aPwA7A4nj366laveWKNreeHn3x/i+wM1JMbYuGamZP8IIYQZSQAoAmnlX7l9EomyBfd/cWKMjRjXMXzOAqo3cAoYeDWC3qT7W5fVtVDfYsdigUEZoe0BJAEg0Z3PNh3k2rfWcrC6fbCnuc3BtW+t5bNNBw3amRCiM3eeMYpBGfEUVzUy77OtRm9HhEBji53b3luP3eHknPED+NH4Abq8b2JsFE9fPIGHzxtLjM3KV1sOc/ZflvDd/qoev7fD4eTJL3cA8Mvpw2QqqRBCmJQEgCLQDq8R8MFmsVhIT4wGoNKXSWCtjdCqMmQMyQACTwZQEBpBaw2gB6TGExtl0/39O6IFgAIqAVv4GBTM6/ixgnnqcRER7A4nD87fTFcJ/w/O3xyyKTFCCN8kxETxhwuOA+CNFXtYXRS8/i3CHB773xZ2ldXTLyWWh849Vtf3tlgsXDZ1CB9cN41BGfHsr2zkwudX8OaK3T0qCft440G2Ha4lOS6KK6fn6rhjIYQQepIAUATyNIAOfgAIID3Bj1Hw2gh4axTEpgRxV13oG7wSMG0E/LCs0PT/ARjoKvMrq2uhqdXHMjyN1QYLHzk6CFQwT91vDU0QSwTf6qKKozJ/vDmBg9VN8uVSCBM6eXgWF584CIC7/v2d/z/rRdgo2F7KGyv2APCnn4wnLSE4mTRjc1L5+MYZnD6mHy12B/d99D03vbs+oDLDNruDp75SvX+unJ5LakK03tsVQgihEwkARaBQjYDXZPgzCt67/49R46b7HqNuaw96AlI6CfUIeIDU+GgSYlSgxu8ysPy5MPseFexZ+CjsWQ6LHle/n32PelxEhN3ldT49r6RWegEJYUa/PfsY+qXEUlRWz5NfBWeIgTBWVUMLd76/AYArThrCjBF9gnq81PhoXvz5Cdx79jFEWS3M33CAH/1lKVsP1fj1Ph+tP8Cu0nrSEqL55fShwdmsEEIIXUgAKMI4nc6QjYDXaI2gfcsAMrABtCYuBdIGq7XOWUC7QzwCHlQ6t6cRdABf3rUgUMHj8PoPYNGjEvyJIPXNbTy7YAe/n+9byWPf5J41GhVCBEdKXDSPnDcOgJcX79Klb4swl3v/s4mS2mZy+yTymx8cE5JjWiwWrpyRy3vXTKV/ahy7yuo577llvP/tPp9e32p38MwC1fvn6pm5JMdJ9o8QQpiZBIAiTEltM7VNbVgtoZtCleFKT/apB5AZAkAA/caqW537AHkmgIUuAAQ6NILOnwsW7ceBRYI/EaC5zc7flhWR/8eF/OmL7TS2OojqYoSwBeifGsfkYQY1ZxdCdGvOmH78aPwAHE6Y+6/vaGlzGL0loZOP1hfz8XcHsVktPHnRBOJjQluCfcKQDD65aQYzR/ahqdXBnf/6jjvf39DthNcP1u5nT3kDmYkxXHHS0NBsVgghRMAkABRhtOyfoZmJIWtC7M4A8qkEzFVyZdQEMI17FLx+k8CcTie7y7QMoNCVgIEnALQ/0ABQwTxwal8knPD17/XZmAg5u8PJv9fs59Q/F/DA/M2U1bUwJDOBpy+ewDMXH48FFezxpv3+/nPGYOsiSCSEMN7954whIzGGrYdqeX5RodHbMZzd4WRFYTkfrS9mRWF5WDayP1jdyH3/UZ9HbjxlOOMHpRmyj4zEGP72ixO5/bSRWC3w/pr9nPfcMgpLOy4hbmlz8MzXalT9tbPySIyNCuV2hRBCBEB+UkeYHYdDNwFMk5GgTQFr7f7JZskA6quNgtevBKy8voW65raQjoDXaI2gA8oA0ho+R8VDm+v1S/4MUXGSCRRGnE4nX2w+zJ+/2Mb2w+rDet/kWG6eM4KLJg0i2qbi/c9bJ/Lg/M3tGkJnp8Zx/zljOHNsf0P2LoTwXWZSLA/86Fhu+sc6nl24gzPHZjMqOzQl32bz2aaDR/086x9mP88cDid3vv8dNU1tjB+YyvWzhxu6H6vVwo2njuCEIenc9O56th2u5Ud/Wcoffnwc54wfgN3hZHVRBSW1TWzYV0VxVSN9k2O5bOoQQ/cthBDCNxIAijA7S0PbABrCsAcQeJWAbQGHA6w9T4bb4zUCPi46tKnbA9JU35biSj8DQFrwZ+adsPiPnvtzJqn7QYJAYWD5zjLmfb6N9fuqANXY89pZeVxx0tCjygjOHNuf08Zkuz/A901WZV+S+SNE+DjnuP78d/0BvtpymLn//o4Prp3W6/4Nf7bpINe+tZYj830OVTdx7Vtref6yiWERBHpjxW6W7iwjLtrKEz+d4A7WG23a8Cw+vWk6N/5jHauKKrjxH+v415r9bDtUw6Ga5nbPnT2qb8g/9wghhAiMOc4yQjehHgEPgU4BywrijnyQkQu2WGhtgMoiXd6yyFX+FcoJYJqcNHXMA9V+BoAcdtXwedxP2t/fVKXud8ioYTPbsK+Ky15ZxaWvrGL9virio23cMHs4i+fO5tf5eZ32kLBZLZyUl8m5E3I4KS+z131xFCLcWSwWHjl/LMlxUWzYV8VrS/U5j4ULu8PJg/M3HxX8Adz3PTh/s+nLwXaW1PLY/7YC8NuzjiGvT+g+u/mib0ocb185hetn5wFqRP2RwR+Af367j882HQz19oQQQgRAAkARZmeIR8ADpCeEYQaQLQr6jlZrnRpBG9UAGjwZQAermnD484F39t0qw6faNe0jdRBYbFC+E8Zfoh4XprOzpJZr31rDuc8tY+nOMqJtFq44aQgFc2dxxxmjSI2XKSxCRLp+KXHce7aaFPWnL7axu6ze4B2FzuqiinZlX0dyAgerm1hdVBG6Tfmp1e7g1vc20NzmYMaILH5u0hKqKJuV204bRXpC1+eVcAi4CSGEkABQRKmsb6GsTgVhQnkVKTPJkwHkdHZz8jdLE2iAvlojaH36ABW5PnyHugE0qC8CVgu02B2U1R19da5b1fvVbd9jYOAktd61UL8NCl0UVzVy5/sbOP3Jxfxv0yEsFrhgYg4Lbp/Fg+eOlRHuQvQyF00axPThWTS3OZj7rw0s31kW1s2QfVVS23nwJ5DnGeEvC3aysbia1Pho/njheCwW82Ziri6qoLKh8z6P4RBwE0IIoUgPoAii9f/JSYsP6SQGLQOo1e6krrmN5LgurhKZJQMIvCaB6RMA2lOulYCFPgMo2mYlOyWOA9VNqiFjip+BgCqvDKABE2HfKihcABMv13+zwm9ldc08t3Anb6/cS4tdTWs7fUw/7jhjFCP79c7mr0IIVQr22AXjOPXPBazeXcmlr6xyPxZuzZD94Wuw26xB8XV7K3luoZqe9fB5Y8lONec+NZEQcBNCCKFIBlAEMaL8CyAu2kaCq9dIl5PAnE6TBYD0mwTmdDrZ7SoBG2ZACRh4RsEXBzIJTMsASh0Ieaeo9a5F0gPIYLVNrTzx5Xby5y3k9WW7abE7OCk3kw+vm8ZLl0+S4I8Qgu8PVLsDw960ZsiR2Jtl8rAM+qfG0VXOjM1qId6EjYkbWtq47Z8bsDuc/Gj8AM4ZP8DoLXUr3ANuQgghPCQAFEF2HDYmAARefYC6agTdUg92V3mSKQJArklgFbugpaFHb1VR30JtUxsAg0M8Al6T05NR8N49gHJOgNgUaKyEgxt03KHwZnc4WVFY3mG5RlOrnZcX72LmvIU88/UO6lvsjMtJ5c1fTeadq6Zw/OB0A3cuhDALrRlyR8KpGbK/bFYL958zpsvn2B1OLnxhOX9dtNNU//2PfbqVorJ6slPieOjcsUZvxyfdBdwsqIyzycNMUN4vhBCiS1ICFkG0ErBQTgDTZCTGUFzVSEV9F/1ntOyfqHiIMSZI0k5SX0jsA/WlULpFBT4CtNtV/tU/Nc6wUahaBtCBqgBSsLUAUNog1SB76AzY9onqA5QzUcddClDjix+cv7ldE9P+qaqha01TG09/tYNDNeqxvD6J3HH6KM4cm23qHhFCiNDzpxnySXkmuPCiozPH9ueWOSN58qvt7e7vnxrHHaeP5KstJfxv0yHmfbaNgm2lPPnTCe7zpFEWbSvhzZV7APjjT44jtZvGymahBdyufWstFmg3fU07K91/zhiZKCmEEGFAAkARZOfhWsCgDKBEbRJYFyVgZir/0vQdA0UFqgysBwEg9wQwA/r/aLQPtvsrAxgFX3NArVMHqtu82SoAVLgQZtyu4y7FZ5sOcu1ba48aX3ywuonr31nn/v2A1DhumTOSCybmEGWTZE0hxNF6e2+WFrsqUz45L5OLThxE32SVhWKzWrhg4kDeX7OfB/77PauKKjjzqcU8cv44w0quKutbmPuv7wD4xbShzBjRx5B9BOrMsf15/rKJR128yI7gXlNCCBGJJAAUIeqb2zjgOiEbEQDKcF3FquxqFLw7AGSiFOF+Y10BoJ6NgtfG7w7NMi6zaWBagCVgtYfA0abGvye7PsBpfYD2rlSlezHGBbYiiVau0VUxgsUCvz3rGH4+dYhh2WRCiPDQ23uzLN1RBsD5Ewdy7oScdo9ZLBYumjSIyUMzuPm99WzYV8WN/1jHwm0lPPijY7seWKEzp9PJvf/ZREltM7l9ErnrzNEhO7aezhzbn9PGZLO6qIKS2qZ2ATchhBDhQS4rR4hCV/lXVlIsaa5+PKHkzgDqqgeQGTOA3I2gN/XobbQSMDNkAPndBFprAJ2SA1ZXwCEjF9IGg6MV9izXcZe9W3flGqB6pY8dkCrBHyFEt3pzb5bK+ha+K64GYMaIrE6fNzQrkX/9+iRuOmU4Vgt8sLaYs55Zwpo9oRtZ/tH6A3yy8SBRVgtP/XQC8THh+/PdZrVwUl4m507I4aS8TAn+CCFEmJEAUITwNIA2JgCR4Qo6+ZYBZKYAkNcoeGfgTSK1CWBGjIDXDEhTV3irG1upa27z/YXuBtADPfdZLJA7W60LF+i0Q9HbyzWEEPrybobc2dfwSO3NsrywHKcTRvZLol9K1xlO0TYrt50+iveuOYmB6fHsq2jkJy+s4Mkvt9PWwQQ1PR2oauS+j9RFphtPGcFxA9OCejwhhBCiKxIAihCeBtDGjIX29AAKswBQn9FgsUJjBdQdDugtnE4nRSYoAUuOiyYlTlV1HvQnC8i7AbS3PC0AtFCH3QmQcg0hhP603izZqe1/bsTYLDx/2cSI7c2ydGcpANOH+95L58ShGXx68wzOPz4HhxOe/noHF724gr3lPZsE2hmHw8md/9pAbVMb4welcf3svKAcRwghhPCVBIAihJEj4EFNAQOoDLcSsOh4yHB9IDv8fUBvUdXQ6h4BPyTD2F457kbQfgWAXCVg3hlAAMPyAYuakFZzUJ8N9nJauUZnIrlcQwgRPGeO7c/Su07hH1dN5b6zjwFUz7HpYdZo2FdOp5PF21X/n67KvzqSEhfNkz+dwNMXTyA5Loq1e6v4wdOL+dea/Th7kAnckb8t382yneXERVt58qLx0tBfCCGE4eRMFCEKDRwBD5Ce4E8GkMm+3Lr7AAUWACpylX9lp8QZXtc/MD2ARtBVWgnYERlACRkw4Hi13iVZQHqwWS3ccfqoDh+TUbpCiJ7QerP8akYuQzITsDth2c4yo7cVFLvLGyiuaiTaZmFKbmCfKc6dkMP/bp7B5KEZ1LfYueP9Ddzwj3VUN3QxzdQPO0tqefyzrQDcc9Yx5PYx5vOZEEII4U0CQBGgqdXuHkNufAZQV2PgXQ0XzZQBBGoSGEBJYJPA3CPgDSz/0rgbQfszCt6dATTo6Me0aWBSBqabHSUqWBt1RJAnOzUuoss1hBChM2ukyvwp2F5q8E6CY+kO9d91wpB0EmICH2g7MD2Bf1w9lTvPGEWU1cIn3x3kzKcXs6KwvEf7a2lzcMt762luczBzZB8umzqkR+8nhBBC6EXGwEeA3eX1OJyQEhdFn+RYQ/aQnqjGqVY1tGB3ODvOYDBjCRh4NYIObBLY7jLjJ4BpBgQyCr6zEjBQfYCW/EllADkcYJWYcU8cqm7i9WVFADz/s4kkxUXLKF0hhO7yR/Xh7yv2ULCtFKfTicUSWT9bFu/Qyr96XuJms1q4fvZwpg/P4pb31lNUVs+lr6zkmpl53HbaSGKi/D/v/WXBDjYV15AaH80fLzwu4v78hRBChC/5NhcBdpZ4+v8Y9SFDKwFzOKGmsZMsILMGgPq6SsBKt4Hd/9RvM0wA0+S4A0A+TpFqqoZmNUa3wwDQwMkQnQj1pVASWImc8HhmwQ6a2xxMGpLOnDH9ZJSuECIopuZmEmOzUlzVSGFpffAOtPAxKJjX8WMF89TjOmuzO1jpytDxt/9PV8YPSuPjG6dz8YmDcDrhhYJCLnh+mfszlq/W7q3kuYU7AXjk/LHdTigTQgghQsnvANDu3bt58803eeihh7j77rt54oknWLhwIU1NMrbYKEY3gAY1YjXZNYGqoqNG0A6HeUvA0oZATBLYW6C80O+X73ZNDxlmphIwXzOAtOyf+HSI7eDvT1QMDJ2u1lIG1iNFZfW8943qt3TXD0bLFWEhRNAkxES5e+Ms2lYSvANZbbDwkaODQAXz1P1W/fvibdhfRW1zG2kJ0Rw7IFXX906MjeIPPz6OFy47gbSEaDYV1/DDvyzh7VV7fGoQ3dDSxm3vrcfhhHMnDOCHxw3QdX9CCCFET/kcAHrnnXeYOnUqubm53HnnnfznP/9hyZIlvPLKK5x55pn069eP6667jj179gRzv6IDRo+A17j7AHXUCLq5Gpx2tTZbE2irFfqqqSmBlIHtLjNPBpDWBPpQTRNtdkf3L+isAbQ39zj4BT3cXe/25y+2YXc4OWV0X04carJ/A0KIiJMfij5A+XNh9j3tg0Ba8Gf2PepxnS1xlX+dnJcVtMzJM8dm8/ktM5k+PIumVgf3fLiJq95YQ3ldc5eve/TTLewubyA7JY7f/2hsUPYmhBBC9IRPAaCJEyfyxBNPcNlll7F7924OHTrEmjVrWLp0KZs3b6ampoaPPvoIh8PBpEmTeP/994O9b+FlpwkygMATAOpwEpiW/RObAlHG9CnqktYHyM9G0FUNLVS7St6GZBqfAdQnKZZomwW7w8nh2q4/qAJQ7UsAyNUIeu8KaPWjt5Bw21RczcffHcRigTvP6HgKmBBC6EkLAK0qqqCxxR7EA3kFgR7MCGrwBzwBID3LvzrSLyWON345mXvPPoYYm5WvthzmzKeXtMuosjucrCgs56P1xTy/aCdvrdwLwJ9+Mp7UhOig7k8IIYQIhE9NoB966CHOPvvsTh+PjY1l1qxZzJo1i4cffpiioiLdNii61mZ3UFRm7AQwTUaCNgmsowCQSUfAa/pqjaD963OjlX/1S4nt0SQSvVitFrJT49hX0ciBqkZ3T6BOddUAWpM1EpIHQO0BFQTSAkLCZ/M+3wbAueMHcEz/FIN3I4ToDYb3TWJAahwHqptYWVTO7FF9g3ew6bepwI/TDtaooAV/appaWb+vSh0yyAEgUOfUK2fkMi0vi5vfXceOkjp+8fo3/GLaUCYOTuOx/23lYHX7FgizR/UJyd6EEEKIQPiUAdRV8OdIWVlZnHjiiQFvSPhnb0UDLXYH8dG27r/sB1m6OwOog0bKZm0ArXFPAvMvA8hM5V+aHH8mgWkZQGldZABZLF5lYNIHyF/LC8tYvL2UKKuF206T7B8hRGhYLBbyXUGfgm1BHgf/yW2etaOt88bQPbSysBy7w8mwrEQGpocu63bMgBTm3zidK05S49z/tnw3N727/qjgD8CibaV8tulgyPYmhBBC+KNHU8A++eQT7rzzTm677Tb+/e9/67Un4QdtOkVe30SsBk8RcvcA6jIDyKwBINcksOq9ajKWj7QJYMNMFADSGkHvr/QlAORDBhB4sn526R8A8k6hX+H6cB8pnE4n8z5T2T+XThnMYBOUCQoheo+Q9AEqmAdr/+75fWKfjhtD6yBU5V8diYu28eC5Y3n18kl093HrwfmbI+pcJoQQInIEHAC67777mDt3LhaLBafTya233soNN9yg596ED3ZoI+D7GFv+BZ5R8B33ADJ5ACg+HVJy1Lpki88v2+MqARtigglgmoH+ZAC5m0AP7vp5w/LV7aGNUKffRJnPNh1k+uMLuOTlldz87noueXkl0x9fEDFXT7/YfJj1+6qIj7ZxwynDjd6OEKKXmTY8kyirhaKyevaUB2EcvNbwOdlr2lV9KUy7KShBoKU7VQBo+nDjSqwSYqPoKrbjBA5WN7G6qCJkexJCCCF85XMAaM2aNe1+/9577/Htt98yb948nnzySebPn8/bb7+t+wZF1wpdAaAR/YydAAaQkagaHoZlAAi8ysB8nwSm9V8aasIMoG5HwdtbodYVaOkuAyipD2Qfp9a7Cnq4Q+WzTQe59q21R6XQH6pu4tq31oZ9EMjucPJHV++fX00fRt/kOIN3JITobVLiopk4JB0IUhaQww4n3+I5l6S5Lib0O1Y1gnbo13x6X0UDRWX12KwWpuYZ91mipPbosq+ePE8IIYQIJZ8DQFdffTW33HILDQ0q4yE3N5cnnniCbdu2sXHjRp5//nlGjhwZtI2Kjmkj4PPCJgPIpE2gAfq6ysD8aAStXVE1YwCo2wygmgOAE2wxKmW/OzqOg7c7nDw4fzMdXUTV7gv3FPoP1u5nZ0kdaQnRXJ2fa/R2hBC91KxRrjKwYPQBmn039BsLONXt2AvV/YULXNPB7tbtUFr2z4RBaaTEGTdhy9dgvgT9hRBCmJHPAaDVq1eTnZ3NxIkTmT9/Pq+99hpr165l2rRpzJgxg/379/POO+8Ec6/iCA6H090DyOgJYNBND6D6cMgAGqtufWwEXd3QSmWDeUbAa3LSXRlAlY04nV0EUNwj4AeC1YcfBbmuANCuhdDV+/pgdVFFh80zNeGeQt/cZuepr3YAcN2sPEO/rAghejetD9DywnKa24IwDn7nV+p2+KmeCwW7FvX4PHGkpQb2//E2eVgG/VPj6KwNkAXonxrH5GEmvuAlhBCi1/J5brXNZuM3v/kNF110Eddeey2JiYk8++yzDBgwoPsXi6A4WNNEQ4udaJvFFAEIzxSwcC0Bc2UAlWxWH1wtXXd51BpA90mOJTHW+BHwmgGpKgBU32KnprGN1IROgg++NoDWDD4JouJUqn/pNug7OuA9RnoK/dsr91Jc1Uh2ShyXnzTU6O0IIXqxMf1T6JMcS2ltM9/uruRkPfvnOBxQ+LVaD58Dg6ZAdALUHVbnUq20uofsDifLCs0RALJZLdx/zhiufWstFmiXyap9arj/nDHYDB7MIYQQQnTE7ybQubm5fP7555x33nnMnDmT5557Lhj7Ej7YcbgWUOVH0bYeDXTTRYarBKy2qY1Wu6P9g+EQAMocAdZoaK7xZMd0wYwTwADiY2xkuoJxXfYB8rUBtCY6DoZMU+seloFFcgp9XXMbzy7cCcAtc0YQF20zeEdCiN7MYrEwc4TKAlq0Tb8m/gAc2qCaPsckwaCpEBULQ05Wj+lQLqz5/kA1VQ2tJMdGMX5gmm7vG6gzx/bn+csmkp3a/hyVnRrH85dN5Myx/Q3amRBCCNE1n6MG1dXV3HXXXZxzzjnce++9XHDBBaxatYrVq1czdepUNm7cGMx9ig7sdDeANr78CyAlPto9GvWoMrBwCABFxUCWq4+VD32Adpe5JoCZIPvqSD41gvYuAfOVTuPgu0uhh/BNoX9lyS4q6lvIzUrkwhP8+LMVQoggcfcB0rsRtFb+NSxfnUPBq19cz84T3rTx7yflZRJlggteoIJAS+86hX9cNZWnL57AP66aytK7TpHgjxBCCFPz+Sx6xRVXsHLlSs4++2y2bdvGtddeS2ZmJn//+9955JFHuOiii7jrrruCuVdxhJ0mGgEPKi06zZUFVFnf6nnA3gZNVWpt5gAQeE0C6z4A5G4AnWWuDCCAAWnqqmSXjaD9LQEDTx+g3UuhrTnA3XlS6Lvyf9OGhl0KfXldMy8v3gXAHWeMMs0XFSFE7zZ9eBZWC2w/XNf9gAB/7NTKv0713KedJ/Ysh1Z9yniX7FCBK6PLv45ks1o4KS+TcyfkcFJeZtids4QQQvQ+Pn87+frrr3n11Vf59a9/zbvvvsvSpUvdj5166qmsXbsWm01KHULJHQAywQh4TXpCB6PgGytdCwvEpYV8T37p5/sksN0mnACmyUlTWUldB4BcGUBpg3x/437HQmJfaG2Afat7sEN19fTpiyccdX9slPqx9PbqvdQ0tR71uJk9t7CQ+hY743JS+cHYbKO3I4QQgOrRN35QGgCL9coCaqzynAeGz/Hc3/cYSMqGtkbYt6rHh2loaWPNHvU5YvoIHyZWCiGEEKJTPgeARowYwUsvvcT27dt54YUXGDJkSLvH4+PjefTRR3XfoOiY0+lkh8kygKCTSWBa+Vd8GtjM0yy5Q9oksJLuJ4HtLjdzCZjKANrfWQDI6fTKAPIjAGSxeE156Xl6f0ZiLKACh0//VKXQr/jNqeSkxbOnvIG7/72x60lmJrK/soG3Vu4B4K4zR2Pppom4EEKEkjYNbJFe4+B3LQKnXZVOp3t9JvQ+T+jQB2jVrgpa7U4Gpscz1ITnWyGEECKc+BwAeu2111iwYAHHH38877zzDs8//3ww9yW6UVbXQnVjK1YL5PYxTwZKekIHk8DCof+PRisBK9vRZep6dWOr+7/RjCVgA12j4DvNAGqoUFk8ACk5/r15rn4f7LW0/lOP6ce5x6sU+oykGJ699HiirBY+2XiQt1bt7fFxQuGpr3bQYndw8vBMppusTEEIIWaN6gvAsp1lRw9qCIR7/Pucox/L1e9CwRKv8e8SWBdCCCF6xucA0IQJE/j222+pr69n2bJlHHPMMcHcl+iGVv41KCPBVFOG3BlA4RgAWvgYrH1Tlak57VC2zfNYwTz1uIvW/ycrKZYkE42A17ibQFd2EgDSyr8S+6rpXv7QruweWK8CST3g/cHe2/GD0/nND9SY+Yfmb2ZTcXWPjhNs2w/X8sFalVE194zRBu9GCCGONi4nlfSEaGqb21i3t6pnb+Z0dtz/R5M7S90e/A7qy3p0qKU71YWC6cOl/EsIIYToqaB0KA2Xko1wtrNEjYA3U/kXqD4DABUdlYCZPQBktcGiRyE2Rf3+sKsMrGAeLHxEPe6ilX+ZNR1dCwCV1DbT3GY/+gmBNIDWJGdD3zGAU5UABKi0tpnNB2sAOHn40Rkzv5o+jDnH9KPF7uD6d9ZSa+J+QH/6fBsOJ/xgbLa7z4YQQpiJzWphxghtGlgPx8GXbIbaAxAV5xn77i25n6ukumfniUPVTWw/XIfFAicPN/lnCCGEECIM+BQAOuaYY3jnnXdoaWnp8nk7duzg2muv5fHHH9dlc6JzngbQ5goAZSR0lQFk8pHe+XNh9j1Q7So5OrzJE/yZfY963GVPmXkngAFkJsa4mykfru5gWlcgDaC96TAOfnmhuip87IAUspJij3rcYrHwp58c5+kH9IE5+wGt3VvJF5sPY7XA7aePMno7QgjRKW0cfI/7AGnlX0NnQHR8x8/RsoB6cJ5YulOdJ47LSXVPGRVCCCFE4HwKAD333HM8+eST9OvXj5/+9Kf88Y9/5O233+bf//43r7zyCrfddhuTJ0/m+OOPJzU1leuuuy7Y++71zNgAGjwlYBUNXtkaWplQQhj0RcmfC6POUusVz3UY/AEock8AM2cGkMViIceVBbS/quHoJwTSANqbuw/QIlUKEACt/KurfjlpCTH8xdUP6OPvDvK2yfoBOZ1OHv/fVgB+csIghvc1179HIYTwpmUAfX+ghpLaHoxo76r/j0a7UNCD88RSV5846asmhBBC6MOnANApp5zCN998wyeffEJ2djbvvPMON9xwAz/72c944IEH2LFjB5dffjn79+/nD3/4AykpKcHed6+nZQCNMNEIeAjzHkCa6be6Fk6wxRwV/AHYo5WAmTQDCCDH3Qi6gw/5Va5ASqABoCHT1J9N9V4oL/T75U6n090AekY3fR0mDk7nrjNVX53ff2yufkCLd5SxqqiCmCgrN88ZYfR2hBCiS32SYxmboz6jLdkeYG+e5jrYs0KtuwoADZkGtlio2a8GK/jJ4XCydKf6/CD9f4QQQgh9+NUDaNq0aTz99NOsW7eOyspKmpqa2L9/P/Pnz+eGG24gLS0tSNsU3qobWympVWU9eSaaAAZePYDCOQC07VPP2t6iysCOsFsrAcs015+/twGpXTSC7kkPIICYBBg8Va0DSO/fWVLH4ZpmYqOsTBqa3u3zr5wxjDnH9KWlzcENJukH5HA4mfeZyv654qQh7r5LQghhZto4+ILtAZaBFS0GRyukDYHMvM6fFx3fo/PE1kO1lNU1kxBjY+KQtMD2KoQQQoh2gtIEWgSXlv3TPzWO5Lhog3fTnrsHUDg2gQYV7Fn6JES5etKceJUqA/MKAtU0tVLuCnANMWkJGHgaQXc4Cr6nASDo0Tj4xa7yr8nDMnyaYqf6AY0nJy2e3SbpB/TJxoN8f6CG5Ngorps13NC9CCGEr7Rx8It3lGJ3BPBzVCv/GnEadDeW3V0G5n8ASJv+NWVYBrFR5pl2KoQQQoQzCQCFIfcEMBP2G0lPVAGphhY7Ta2u6VPhEgDybvjc91h1X26++r1XEGivq/wrKynGdAE4b+4SsOojAkCtTVDvmgCTNjjwA2gf7IuWgN2/jBytr8PMEb6n9aclxPDMJZ5+QO+sNq4fUKvdwZ+/2AbA1TNz3ZlvQghhdscPSiM5Loqqhla+21/l34udTtj5pVp3Vf6lyXNdKNjt/3nC0ydOyr+EEEIIvUgAKAy5J4CZMACUFBtFtE1dEXRnAbmbQJt8CpjD7mn4nJGr7qvY5ZkO5lABrSJX+dcQE5d/AQxIiwM6KAGrKVa30QkQ3335Vaeyj1NBvZZaKF7j88ua2+ys3KX+Tvjb2POEIenMPVNN2npw/ma+P2BMP6B/fruP3eUNZCXF8MvpwwzZgxBCBCLKZmX6cPWz1+8ysPJC1UPOFqMmgHWn3zg1AKKlDvZ/4/NhmlrtrC5S54mZ0gBaCCGE0I0EgMKQmQNAFouFdFcZWHldC7Q1qwABmD8DaPbdnobPWgBIa3CcP1c9Duwp1wJA5i3/AhiYpvZXXNXYvlzKuwF0d+n7XbFaYVi+WvtRBrZ2TxWNrXaykmIZne1/E/Mrp+dy6mitH9A66prb/H6PnmhssfP0V6qh6Y2njCAxNiqkxxdCiJ7S+gD5PQ5ey/4ZfBLE+vAZxGpVmbTgVxnYt7sraW5z0C8l1pSfdYQQQohwJQGgMGTWEfAa9ySwhhZP+ZfFBnGpBu7KT1pjy4pdRz2021UCNszkGUD9UmOxWKC5zeHuWQTo0/9HE0B/B62vw4wRWVgCCEBZraof0IDUOIrK6vltiPsB/W35bkpqmxmYHs8lk3tQQieEEAbJH6UCQBv2V7Wf2tkdX8a/H0k7T/jRCHqJ6zwxfXifgM4TQgghhOhYQAGgwsJC7r33Xi655BJKSlQvkc8++4zvv/9e182JozW0tFHsauprthHwGi0DqKK+pX3/n3D6EOddAnYEbQLYEBOPgAeIjbLRJ0k1s27XCFrXAJCrv0Pxt9BY5dNL3H0dhgee1p+eGMNfLj0em9XCfzcc4B+r9wX8Xv6obmjl+UU7Abj99JHEREkMXQgRfvqnxjOqXzJOJyzZ6eM4+NZG2L1Urf0JAGkDA4rXQGOlTy/RRtTPHCnlX0IIIYSe/P72UlBQwLhx41i1ahUffPABdXUqG+W7777j/vvv132Dor1dpfU4nSrLJsOkjWfdGUBHBoDCSYYrA6imWH3o9RIuGUDg1Qi6XQDIFSxJG9TzA6QOhKyR4HSoJp/dqKxvYWOx6tszo4d9HU4YksHcM1Q/oAfmf8/mAzU9ej9fvLC4kJqmNkZnJ/Oj8TlBP54QQgSLlgVU4GsZ2J5l0NYEyQOg7zG+Hyg1x3OeKOr+PFFW18zmg+rn+ck9uFAghBBCiKP5HQD6zW9+w8MPP8yXX35JTIwnADF79mxWrFih6+bE0czc/0ejTQKraGgN3wBQQgbEukrWKne7765taqWsrhmAwSbvAQSeUfD7KzsIAKXqEAACr3Hw3af3Lyssw+mEUf2S6ZsS1+NDXzUjl9mj+rj6Aa0Naj+gwzVNvL6sCIA7zxiFzRpGGW1CCHEErQ9QwfZSHL6Mg9+hjX+f439Grx9lYMtcGUnH9E8hy5XFKoQQQgh9+B0A2rhxI+eff/5R9/fp04fy8nJdNiU6t8PEI+A1GQneGUBhMgHsSBYLZLimO2mNoIE9ruyfjMQYUuPNOwJeMzBNywBq8txZpXMASCsD86ER9FJX+VdPs380VquFP180gf6pcewKcj+gZ77eQVOrg0lD0jlldN+gHEMIIUJl0tB0EmJs7TJuuhRI/x9Nrv/nCZn+JYQQQujP7wBQWloaBw8ePOr+devWkZMjJRHBpmUAjTBxACjdVQJW0RDGJWDQYSNoLQA0NAyyf8CTAVRcpfaNw+EZA69HDyCAodPBGgWVRVBR1OnTnE6np/+Pjh/sMxJj+Mslnn5A736jfz+gorJ69/ve9YPR0pRUCBH2YqNsTMtT5+Zux8FX7obyHWqggzb90R9DT3adJ3Ybcp4QQgghhOJ3AOjSSy/lrrvu4tChQ1gsFhwOB8uWLeOOO+7g8ssvD8YehZcdYVACFhE9gKDDRtC7XSPgh4ZB/x/wBIDcGUD1pWBvAYsVUgboc5DYZBg4Wa27SO8vKqunuKqRGJuVKcP0/fswaWgGd2r9gP77PVt8uZrthye+3I7d4eSU0X05cWiYZbMJIUQnvMvAuqRl/wyaDPFp/h8oNhkGTVHrLs4ThaV1HKppIibKKj9rhRBCiCDwOwD0yCOPMHjwYHJycqirq2PMmDHMnDmTadOmce+99wZjj8Klpc3hzkAZ0decE8Cgiylg4cYdAPKUgLkngIVJACgn7Ygm0Fr/n+T+YNOxhM2HcfBLXX0dJg1NJz7Gpt+xXa6ekcusUX1obnNw/dv69QPaVFzN/A0HsFhwB5mEECIS5I9U5axr9lRS09Ta+RN3fq1uAyn/0vhQBqZl/0wemkFctP7nCSGEEKK38zsAFB0dzdtvv8327dv55z//yVtvvcXWrVt58803sdnkZB1Me8rrsTucJMVG0S/FvI0R3RlA4V4Cpk0C80pXd5eAZYVHCZgWACqvb6Gxxe7VAFqn8i+N1geoqAAc9g6fsnh7cNP6rVYLT1w0gewU1Q/ong/16Qf0x8+3AXDu+AEc0z+lx+8nhBBmMTgzgdysROwOJ8s7Gwff1gK7CtS6JwEg93licafniSU694kTQgghRHt+B4A0eXl5XHjhhVx00UWMGDFCzz2JTmjlX3l9k0zdgyTdXQLWijOsA0CuDKDq/dCqSqiKwqwELCU+ikRXts2B6kb9G0BrBhwPcanQVA0H1h31cKvdwcpd6u/CzBF99D22l4zEGP5yqeoH9NH6A7zXw35AKwrLKdheSpTVwm2nSfaPECLyzOyuDGzfSmith8Q+kH1c4Afq5jzR0uY5T0j/HyGEECI4ovx9wW233dbh/RaLhbi4OIYPH865555LRobUbustHBpAg2cKWIvdgbO+HAuE3xQwgMQsiE2B5hqo3E196nBKa9UI+HAJAFksFnLS49l+uI7iykbyqverB/TOALK6GoNu+a8qAxs4qd3DG/ZVUdfcRkZiDGOCnEVz4tAMbj99JPM+28b9//2eCYPTGJ3t/zGdTifzPt8KwKVTBjM4TBp/CyGEP/JH9eFvy3dTsK0Up9N59AWmHV+q27xTwRrwdcMjzhMLjjpPrNtbSUOLnczEGI4J4Ge2EEIIIbrn95l83bp1vPrqq7z00ksUFBSwaNEiXn75ZV599VW+/vprbrvtNoYPH87mzZuDsd9eLRwaQAPEx9iIj7YBTs8Y+MQwvJrnPQq+Ype7AXR6QjSpCeYfAa8Z4N0HKFgBIOhyHPxiV1r/tLxMrNbgZ6/9emYe+SNVP6Dr3l5LfQD9gL7cfJh1e6uIj7ZxwynDg7BLIYQw3tRhmcREWTlQ3eT+nNGO1v9nxGk9P5j7PHF0vzjv6V+hOE8IIYQQvZHfAaBzzz2XOXPmcODAAdasWcPatWspLi7mtNNO45JLLqG4uJiZM2dy6623BmO/vVq4ZACBKsWJpxmr3TV9KhxLwKBdI2it/0+4NIDWtGsEXb1X3Zk2WP8DaY2g96+G5tp2Dy3doUoLgln+5U31Axqv+gGV1nPvfzb51Q/I7nC6e//8avow+ibHBWurQghhqPgYG1NzXePgtx1RBlZzAEq+ByyeJs49ob1HB+eJJa4eRNOHh+EFIyGEECJM+B0A+uMf/8hDDz1ESoonPTclJYUHHniAefPmkZCQwO9+9zvWrFmj60Z7O7vDSWFpeGQAAaQnRpOB68NdVBxEh2n5jLsRtCcDaFhWeAWAtAyg/cHOAEofCunDwNEGu5e6765ubGX9viogtH0dMpNieeYS1Q/ow3XFvP/tfp9f++G6YnaU1JGWEM3V+blB3KUQQhiv03HwWvZPzkRI1OFCTsYwr/PEMvfd1Q2tbNxfBcCMEF0oEEIIIXojvwNA1dXVlJSUHHV/aWkpNTU1AKSlpdHS0tLz3Qm3/ZUNtLQ5iI2yMjDd/MGU9IQY0i2uAFBCpiqnCkfuDKBdXiPgzf/n7809CayiAhor1Z16N4HWdJDev6KwHIcT8vokuoNRoTJ5WAa3nTYSgPs+2sTWQzXdvqa5zc6TX24H4LpZeaTEhU+5nxBCBEILAK0uqqChxatkdqer/09Ppn8dqYNy4eWFZTicKsM5O1UyLoUQQohgCagE7Je//CUffvgh+/fvp7i4mA8//JBf/epXnHfeeQCsXr2akSNH6r3XXm3HYZX9k9snCVsY1MZnJMaQ4Q4AhWEDaI0WACrfxW5XCVi4ZQDlpKugi0ObABabCnFBarCplYHt8gSAlu5UV5SNuqp7bX4eM139gK73oR/Q2yv3UlzVSHZKHJefNDQ0mxRCCAPl9UlkYHo8LXYHKwpd0zvtbVC4SK2H69D/R6OVgXmdJxZ79f8RQgghRPD4HQB68cUXOfXUU7n44osZMmQIgwcP5uKLL+bUU0/lhRdeAGD06NG88sorum+2N9tZGj79f8CVAYRXBlC4ynSVgFXv40BZFRB+PYC0rJvoumJ1RzDKvzRDZ4DFCmXb3eVm7saeBvV1sFotPHnRePqlxFJYWs99XfQDqmtu49mFOwG4Zc4I4qJtodyqEEIYwmKxHF0GVvwtNFdDXJoqAdPLsJlHnSc8FwokACSEEEIEk98BoKSkJF5++WXKy8tZt24da9eupby8nJdeeonERPXFeMKECUyYMEHvvfZqO8NkApimfQZQGAeAEvtATBLgJLZOZdAMDbMSsH7JsdisFrKdKhBDWpDKvwDi0yDHNdq3cCF7yxvYU95AlNXC1Dzj/h5kJsXyzMXHY7XAB+uKeX9Nx/2AXlmyi4r6FnKzErnwhCAGyoQQwmSOCgDt/Erd5p2iRrjrJT4Nck5Q68KF7CmvZ19FI9E2C1OGhfHnBSGEECIM+B0A0iQlJXHccccxfvx4kpLCIygRznaE0QQwgPRICQBZLO4ysKGWQ6QlRJOWEGPwpvwTZbOSnRLHAIsrABTMDCBo199hieuq7sTB6STFRgX3uN2YkpvJ7aePAuB3H21i26H2E2jK65p5efEuAO44YxRRtoB/PAohRNiZNjyLaJuFPeUNFJXVww5X/x89xr8fyasMTCv/mjg4nUSDzxNCCCFEpAvoTPvNN9/w/vvvs3fv3qOaPX/wwQe6bEwodoeT1UXlbD2omteGS/+ZjIQYrJFQAgYqAHToO4ZaDlMWZuVfmgFpcQyod/V1CFYDaE3ubCh4HIoKWNakGsabJa3/2vw8Vu4qZ8mOMq5/Zy0fXjeNTcU1lNQ28dmmQ9S32BmXk8oPxmYbvVUhhAippNgoJg3JYMWuclZt3Mqwg+vVA1pvNz3lzYbF82DXIpY1/Bowz3lCCCGEiGR+X+J+9913Ofnkk9m8eTMffvghra2tbN68mQULFpCamurXez3wwANYLJZ2v7KzPV+8nE4nDzzwAAMGDCA+Pp5Zs2bx/fff+7vl8LTwMXb88z6mP76AS15eRXObA4BfvP4NO/55Hyx8zOANdi09Mbr9FLBw5pUBFG7lX5qctHhyQpUBNHASxCRDQznlhd8C5mnsabVaePKnE7gn4T/8oPwNJj/yNZe8vJKb313P/zYdAuDuxP9iWfQHg3cqhBChlz9KlYHVbPpc3ZE9DpKDEBAfeKIqr24op2LXGgCmy/h3IYQQIuj8DgA9+uijPPnkk3z88cfExMTw9NNPs2XLFi666CIGDx7s9waOPfZYDh486P61ceNG92Pz5s3jiSee4Nlnn+Wbb74hOzub0047jdra2i7eMTLsKG1gxOZnuLDunXb3X1T/DiM2P8OO0gaDduabiJkCBu5G0EMshxkathlA3gGgIGcA2aJh2AwAJrauJyUuiuMGpgX3mH7ISopl7KAMbo/+F1c63m/32I22D5i290XT//sSQohg0PoA9S9dqu7Qc/y7N1u0GhqAOk+kxkczLse/i4hCCCGE8J/fAaDCwkLOPvtsAGJjY6mvr8disXDrrbfy0ksv+b2BqKgosrOz3b/69FEfPpxOJ0899RT33HMPF1xwAWPHjuXvf/87DQ0NvPPOO928a3izO5xcXjiLP7deyO3R/+JGmyqru9H2AbdF/4snWi/k8sJZ2B0dTzIygwyvKWCOuAjKAMoKzwygganRZFOhfhPMJtAaV8nAdOtGTh6ehc1qCf4xfWR3OLnt0Okd/vu6PUz+fQkhRDCMzk6mf3IUJ1s2qDv0HP9+JFe/OHWeyDTVeUIIIYSIVH73AMrIyHBn4OTk5LBp0ybGjRtHVVUVDQ3+XzXfsWMHAwYMIDY2lilTpvDoo4+Sm5tLUVERhw4d4vTTT3c/NzY2lvz8fJYvX84111zT4fs1NzfT3Nzs/n1NTY3fezLa6qIKDlY38RcuAOD26H9xW9S/sFjgz60X8hf7BVDdxOqiCk4ycLJSV9ISYrC4MoDqolJIMXg/PZKhMoByLGVUpEUbvJnADI2tI8rioA0bUUn9gn9AV4PPSdZt7Ms1V9ZUR/++bon6NzaLM2z+fQkhRDBYLBYuGVRJxq46mq0JxA6aHLyDuS4UnGjdRvGw8BhwIYQQQoQ7vzOAZsyYwZdfqskQF110ETfffDNXXXUVl1xyCaeeeqpf7zVlyhTeeOMNPv/8c15++WUOHTrEtGnTKC8v59Ah1Y+jX7/2X1b79evnfqwjjz32GKmpqe5fgwaFINtBZyW1Te71X+wXYHdasFigzWlVX047eJ7ZxNgs7gygSmeywbvpmcaYTOqdsdgsToZFlRu9nYAMtKp9HyJT33G+nahLGkKxM4tYSxunxu0M+vH84f3vphxVcmCzOMPq35cQQgTLGbGq1+Jq63GqVCtIahOHcMCZSayljVPizXWeEEIIISKV3wGgZ599losvvhiAu+++mzvuuIPDhw9zwQUX8Oqrr/r1Xj/4wQ/48Y9/zLhx45gzZw6ffPIJAH//+9/dz7FY2qcEO53Oo+7zdvfdd1NdXe3+tW/fPr/2ZAZ9k+Pc6xttH2CzqFKUKIvDXa5y5PNMp6maKItqXF3uCO8re3sqG9jjVE0wUxr2GLybwPR1qGlc+x2Z1Da1Bv14q4oqWGwfB0Cf0hVBP54/tH83x1j28Puo1933R1kc3G5776jnCSFEb5JXrX5mf9p4LPsrg9cPbWVRJUu080TJ8qAdRwghhBAefgeAMjIyGDBggHqx1crcuXP573//yxNPPEF6enqPNpOYmMi4cePYsWOHexrYkdk+JSUlR2UFeYuNjSUlJaXdr3AzeVgG/VPjuMnVk+TjtikAFDsyuT36X9xk+4D+qXFMHmbi5soNKuOkzhlHebPff81MZXdZA0VO9XfOUlFk8G4CE1d/AIBiZyYHqoKf2bJkRxlLHeqDPYULgn48f0welkFeipN3oh8hyuJglyObQkd/AG6M/ig8/n0JIUQwNFQQdVBN5Sqwj6dge2nQDrV0RylLtPPErkVBO44QQgghPPz+Zm6z2SgpKTnq/vLycmy2npWWNDc3s2XLFvr378+wYcPIzs52l5sBtLS0UFBQwLRp03p0HLOzWS28kbfI3fD5Wfv5ACRamnii9UJui/4Xb+QtMnfDxAbVcLjSmUxlfYvBm+mZ3eX17gwgKnYZu5lAVatMuGJnFgeqGoN+uCU7SlnmOBYnFijZDLWdl22Gms0CH8Y9SLq1jhpnPBe0PMgf234KQLMzKjz+fQkhRDDsWgROB+UJuRwgi4JtwQsALdlZ5jlPHN4EtYeDdiwhhBBCKH4HgJzOjifjNDc3ExMT49d73XHHHRQUFFBUVMSqVau48MILqamp4YorrsBisXDLLbfw6KOP8uGHH7Jp0yZ+8YtfkJCQwKWXXurvtsPOiD4J7BhzE+8nXUqRMxu700KapZ6vE3/AjjE3MaKPyadRuTKAKkimoiG8A0B7yuvZ7coAoqLQ2M0Eqno/AAecWewPcgDoQFUjhaX11FiSsWePV3cWLgzqMf2y9u+k1GzHgYU7Y+6limQ+c5zIBkcusZY2KjMmmP/flxBCBMPOrwFw5KoGzcsLy2lpc+h+mOKqRnaV1lNjTcXRT7KAhBBCiFDxeQrYM888A6iePK+88gpJSZ6+Lna7ncWLFzN69Gi/Dr5//34uueQSysrK6NOnD1OnTmXlypUMGTIEgLlz59LY2Mh1111HZWUlU6ZM4YsvviA5ObybCvtk9t2MAJY6nKwuqqDxg0Ek1e/lvz/tgy3vIaN31z1XACgiMoDKGrA7wj0DSAWAip1ZpAc5ALR0RxkAEwalETXiVDi0HnYthAmXBPW4Pjm0ET6dC4B1zgP8ddr1rC6qoKS2iai6B+Grn5NevRkmvGHwRoUQIsScTtj5FQCZ488mY4udivoW1u6tZGquvhMRl+5QmUXjB6ZiG34KHP5OnSfG/1TX4wghhBCiPZ8DQE8++SSgMoBeeOGFduVeMTExDB06lBdeeMGvg7/77rtdPm6xWHjggQd44IEH/HrfSGKzWtQo6pxjYftebOXbIS/f6G11zxUAKieZinAPAJXXY9dKwKr2QlsLRPmX7WYopxOqVAnYAWcm6ZXBDQAt2akCQNNH9FHj4Jf8WWUAOZ3QRQP3oGuuhfd/AfZmGH4aTLvJ8+8LgBzYNUtdhV70Bzj/eeP2KoQQoXZ4E9QdgugErEOnMXPEFv6z/gCLtpXqHgBassPrPJF3Cix7yhznCSGEECLC+VwCVlRURFFREfn5+WzYsMH9+6KiIrZt28bnn3/OlClTgrnX3q3PKHVbus3YffjKOwMojEvAmlrtHKxuooQ0nNEJ4HSoIFA4aaqGllpABYCC2QPI4XCyzBUAmjEiCwZNhugEqC+Bw98H7bjdcjrh41uhfCckD4DzXwRrBz/+Tv2dut3wDzi8ObR7FEIII7myfxg6A6LjmDWqL4DujaC9zxMzR2TB4KkQFa+CTyVbdD2WEEIIIdrzuwfQwoULezztSwRACwCVhVcAqMIZ3hlAeyvUCNzkuGjIGKbuDLcyMFcD6Na4DBqJC2oAaPPBGirqW0iKjWLCoDSIioWh09WDuwzsA7T2Ddj4PlhscOFrkNjJ1eycE2DMuYATFjwc0i0KIYShXP1/GD4HUEF8iwW2HKzhcI1+0yO/P1BDZUMrSbFRjNfOE0Ncwz2MPE8IIYQQvYDfASC73c6rr77KpZdeypw5czjllFPa/RJBkhVuGUCuKWAkU9nQavBmAldUVg/A0MxELBm56s5wawTt6v9DykAADtU00WrXv6knwGJXX4epuZlE21w/XnJnq1ujxsEf2gT/U31/OOVeGHJS188/5T4VKNr2CexbHfz9CSGE0ZpqYO8KtR6hAkCZSbGMy0kFYLGOWUBLdnZwnshzfX406jwhhBBC9BJ+B4Buvvlmbr75Zux2O2PHjmX8+PHtfokg6TNS3dYdhsZKY/fiiwjJANpT7goAZSVCRp66M+wygFQAKCpjMDE2Kw4nul7N9aY1gJ45Mstzp/bBfs9yaA3OcTvVXKf6/rQ1qavaJ9/S/WuyRsDxP1Prrx5Q5WNCCBHJihaDow0yctUvl1kj+wCwSM8A0PaOzhOuCwW7l0Fbs27HEkIIIUR7PjeB1rz77rv885//5KyzzgrGfkRnYpMhJQdqiqF0Oww2eb+lBvUBr9KZTHVjK612h+dKXxgpKlMlYEMzEzwfisMtAOTqWWRJHUT/tDj2lDdQXNnIwHR9R503ttj5drcKTk4f7vXBvs8oSO4PtQdh30rInaXrcTvldMInt0H5jq77/nQk/zew4T3Ys0yVRbiuiAshRETS+v8Mb/+zLn9UH55ZsJOlO8poszuI6uF5vLHFzpo9HZwn+o6BpH7qIte+VTBsZo+OI4QQQoiO+X0mj4mJYfjw4cHYi+hOOPUB0ppAW5IBqArTMjAtA2hIZqInAFQepiVgqQMZkBoPwIFq/fsArSoqp8XuICctnmFZiZ4HLBZjysDWvQnfvefV9yer+9doUnNgytVq/fUD4AhOyZwQQhjO6Tyq/49m/MA0UuKiqG5sZcP+6h4fyrfzhPQBEkIIIYLF7wDQ7bffztNPP41TyiJCL1z6ANnboLFKLWMzAMJ2EtiecpUBNCwrATJdJWBVe8EeRgEtVxNo0gaRk+4KAFXpX4qllX+pxqFHjPF193cI0Qf7w9/Dp3eq9Sn3dN/3pyPTb4PYFDi0Eb7/QN/9CSGEWZRth+q9YPNq2u8SZbMyY4QqA9NjGljX5wmD+8UJIYQQvYDfAaClS5fy9ttvk5eXxznnnMMFF1zQ7pcIonAZBd9UBagAoTVRBYDCsQ9QU6vdnSkzJDMRkrLVqFqnPbxGwXtnAKWpAND+Sv0zgJa4PthPH9FBpo1W9nXoO6jTd6TwUbz7/uSdCiffGtj7JGTAtJvUesHD4RX0E0IIX2nlX0OmQUziUQ/nj3IFgLaV9PhQPp0nDm5wD5IQQgghhL78DgClpaVx/vnnk5+fT1ZWFqmpqe1+iSAKlxIwV/kXcWmkJaqAQ2UYBoD2VTTgdEJybBSZiTGqf0y49QFqa4HaQ2qdOpictDgA3UfBl9Q0se1wLRYLnJzXwQf7pD6QPU6tiwp0PXY7Tid8cru6op3cHy54yfe+Px2Zei0k9oHKIjVKXgghIk0n/X80+a5G0N8VV1NeF3iD5m7PE8nZqhcQTti1KODjCCGEEKJzfjeBfv3114OxD+GLPqPVbdVeaKnv8EqdKWgBoIRM0hNjAKgIwxKw3a7yryFZCZ5U9YxhUPJ9+ASAaooBp0rtT8wiJ039vynWOQCkXdUdl5Pq/n9+lNzZqpyqcCGMu1DX47utfxu+excsVvjxq/71/elIbBLMnAv/uxMKHofxl0CMvs2zhRDCMC0NavIWdBoA6pcSx+jsZLYeqmXpzjLOnZAT0KGW7vThPJF3CpRsVmVgYyWrXAghhNBbQJfG29ra+Oqrr3jxxRepra0F4MCBA9TV1em6OXGEhAxIcH2hLdth7F664hUAykhQH/LCMQNod5lXA2hNuDWC9ir/wmJhgFcGkJ59vLQP9jM6SuvXePd3CEYPscOb4ZM71Hr2PTD0ZH3e94RfQNpgNZ1m1Qv6vKcQQpjB7qVgb4bUQZ4s4w5oZWCLtgVewusu/xrexXlCawS9a1FwzhNCCCFEL+d3AGjPnj2MGzeOc889l+uvv57SUvVhYN68edxxxx26b1AcIRz6AHWUAVQffv1TdrsmgA3zDgBpjaDDJQPIqwE04O4B1NBip7pRn/8nTqfT64N9n86fOPgkiIqD2gOqREtPLfWuvj+N6gry9Nv0e++oGJh9r1ovewoaK/V7byGEMJK7/OtUNYmrE7NG9gVg8fZSHA7/AzNOp9N9oaDD/j+aIdPAFqPOXeFyoUUIIYQII34HgG6++WYmTZpEZWUl8fHx7vvPP/98vv76a103JzoQDn2AvDOAEqOB8JwCtts9At6r5CfcegB5ZwABcdE2spJUUE6vRtBbD9VSVtdMfLSNiUPSOn9idLwKAoH+U14+uUP9m0juD+f3sO9PR8ZdCH2PhaZqWPqUvu8thBBG6ab/j+aEIekkxtgor2/h+wM1fh9m2+FaSmvVeeKEIemdPzEmAQZPVWuZBiaEEELoLqApYPfeey8xMe3rt4cMGUJxcbFuGxOdCIdR8Nr0joQM0hO0DKAwDACVqR5AQ7M6KAGr2qPG3ZudlgGUOth9l5YFpFcjaG2s79TcDGKjbF0/ORjj4Ne9DRve8fT9SeoiCylQVhuc+ju1XvUC1BzQ/xhCCBFKFbugohCsUTAsv8unxkRZmeYq3SrY7v80sCXb1Xliii/nCXcZmI7nCSGEEEIAAQSAHA4Hdrv9qPv3799PcnKyLpsSXQizErAMVwlYuGUANbd5RsAP9S4BSx6gypgcbVAdBqPgq7QA0ED3XTmuAJBejaAX71BloNNH+BB40foA7V6qJpT1VMkWNfULYPZv9ev705GRZ8CgqWq8fMG84B1HCCFCYacra3vQVIhL6fbp2jSwQPoALdnpQ/8fjXaeKFoC9vArHxdCCCHMzO8A0GmnncZTTz3l/r3FYqGuro7777+fs846S8+9iY5oAaCKXfp8gQ6GDgJA4ZYBpI2AT4zxlEwBqrQofZhal4dBGdgRJWCgbwZQU6ud1UUq42tmV30dNH2PVWPVW+th/+qeHbylHv55her7kzsbpt/es/frjsUCcx5Q67VvSH8KIUR48+7/4wMtALR2byXVDb4HZtR5Qn0umOHLhYLs8RCfAS21sP9bn48jhBBCiO75HQB68sknKSgoYMyYMTQ1NXHppZcydOhQiouLefzxx4OxR+EtuT/EpoDTrlK3zaijDKAwCwB5l39ZjmyMGS6NoJ1OTwDI1QQaPBlAB6qaenyINXsqaW5z0C8lluF9k7p/gdXqSe/vaRnYp3eqvj9J2XDBy/r3/enIkJNgxBnq39+Ch4N/PCGECIa2ZiharNbd9P/RDMpIIK9PIg4nLCss8/lQa/dU0tTqoG9yLCP7+XqemKXWUgYmhBBC6Mrvb0wDBgxg/fr13HnnnVxzzTUcf/zx/OEPf2DdunX07ds3GHsU3iwWyBqp1mYtA9MCQIlZ7ilg9S12mlqPLh00K60BdLvyL02GKwPI7AGghnKVHQOQkuO+W8sA2q9DBpC7/Gt4n6MDZZ3xHgcfqHVvw/q3XX1/XglO35/OnPo7wALffwAH1ofuuEIIoZc9y6G1AZL6QfY4n1+W75oGVuBHGdjiHZ7pX/6fJyQAJIQQQugpoEvm8fHx/N///R/PPvssf/3rX7nyyivbTQQTQdZntLo1bQBIawKdSXJsFFFW9YGvyo+UcSPZHU5W7lJBrCirBfuRI2/dk8BMmoGl0RpAJ2VDVKz77hwdS8C0BtAzR/pQ/qXRMoAOrPP8XfFHyVb49A61nvVbGDbD//foieyxMO4nav3170N7bCGE0IP39C9fgzJA/igVbC/YXorT6ds4+KU7VbBohi9lwhrtPFG8BhqrfH+dEEIIIbrkdwDoscce47XXXjvq/tdee01KwEKljysDyIyj4NtaoNk1IjYhA4vF4s4CCoc+QJ9tOsj0xxfw1RY15eSjDQeY/vgCPtt00POkjDApAeugATRATroKAJXWNvcoK6usrtk9DvhkXxp7alL6Q59jAKenBMFXLfXw/hXqynXubJhxm3+v18vs36rJOYVf+//fIIQQRtMaQPvY/0czZVgGcdFWDtU0se1wbbfPLw/0PJE2CDJHqHLb3Uv82qMQQgghOud3AOjFF19k9OjRR91/7LHH8sILL+iyKdENM4+Cb3RldFhsEJsKQEZCeEwC+2zTQa59ay0Hq9v3xjlU3cS1b631BIG0DKBKk4+C76ABNEB6QjRx0eqf/qHqwPsALXNNdRnTP4WspNhunn2EQMvAPp0LpVtV2cIFL6nx7EbIGAYn/J9af/Wg6rckhBDhoHo/lG5RJbRapo2P4qJtTM3NBHwrA1tWWI7TCaOzk+mbHOffPqUMTAghhNCd3wGgQ4cO0b9//6Pu79OnDwcPHuzgFUJ32iSwsh3gMFlfHXcD6Ax3U970xGjA3BlAdoeTB+dvpqOv8dp9D87frMrBUnLAFguOVk+ZlRlpe/NqAA1qcp8ek8C08i+/0vo1eaeo28KFvgdP1v8D1r/l1ffH4J5jM++E6AQo/ha2fmLsXoQQwlda+VfOJHWu9pM2Daxge/cBoKU7Aij/0mjBKWkELYQQQujG7wDQoEGDWLZs2VH3L1u2jAEDBuiyKdGNtMEQFQf2ZqjcbfRu2qt3TQZJyHTf5Z4EZuIMoNVFFUdl/nhzAgerm9TIc6s1PBpBawGg1EFHPZTTw0bQTqeTJV6NPf02ZBrYYqB6r29/hqXb4BNXudesu2HYTP+PqbfkfjD1OrX++vfmC8YKIURHvPv/BGDWKBV8/2Z3BXXNnWfBOp1OrwsFATTqHzpdldpW7DLfZx0hhBAiTPkdALryyiu55ZZbeP3119mzZw979uzhtdde49Zbb+Wqq64Kxh7Fkaw2yBqh1mXbjd3LkbxGwGvSXSVg5XXmDQCV1PpWCuV+nrsRtJkDQFoJWOcBoEAzgApL6zhU00RslJUTh/p/BZmYRBg0xfVm3ZSBtTTAP119f4blw4zb/T9esJx8E8Snq35cG941ejdCCNE1eyvsKlDrAANAQzMTGJyRQKvdyYrC8k6fV1haz4HqJmKirEweFsB5Ii4FBp7oejPJAhJCCCH04HcAaO7cufzqV7/iuuuuIzc3l9zcXG688UZuuukm7r777mDsUXTE3Qdoq7H7OJJ3CZhLOGQA+dqbwP28cAgAddIEGjwBoOLKwAJAi7erq7qTh2UQFx1gHx6tv8OuRV0/7393qn4VSf1U6ZdRfX86EpcK012ZSYseg9bAeyoJIUTQ7VutBjXEZ8CACQG9hcVi8SoDK+n0eVr514lD0wM/T0gZmBBCCKErvwJAdrudxYsXc9ddd1FaWsrKlSvZsGEDFRUV/O53vwvWHkVH3KPgzZYB5BkBr9EygMzcA2jysAz6p8bR2TBcC9A/Nc5zFdPsAaDWRmhwleN1EABy9wCqDiwAtHRnD/r/aLQP9kWLO2+mveFdWGeivj8dmXwVJA9QJXffHj0hUQghTMNd/nVqj4LpWgBo0bbOx8Fr54npwwMo/9Jo/eJ2FUiZrRBCCKEDvwJANpuNM844g+rqapKSkjjxxBMZO3YssbF+TgASPaeNgjdtBlB49QCyWS3cf86YDh/TgkL3nzMGm9X1Oy0AVF4Y/M0ForpY3cYkqRKlI3iaQPufsdLS5mDlLvX/uUcf7PuPV1ehm2ugeM3Rj5dug49vVev835ij709HouNh1m/UesmfoKnG2P0IIURnetj/R3NSXiYxNiv7KxvZVVZ/1OOtdoe7PKxHFwoGHK8mijZVwYH1gb+PEEIIIYAASsDGjRvHrl0mzXroTbQMoLId5hpB3VEPoEQtA6jViB357Myx/Xnq4glH3Z+dGsfzl03kzLFe0+8y89Rt5W5zXpWs3qtuUweC5ei8poHprhKwqkYcDv/+/qzdW0lDi52spBhGZycHvkerDXLz1frIPkAtDfD+L1x9f2bCzDsCP04oTPgZZA5Xf/9XPGf0boQQ4mi1h+HQd2qtZdYEKDE2ihOHqYsLHY2DX7e3ivoWO5mJMYzpnxL4gWxRMGyGWu/qpl+cEEIIIbrldwDokUce4Y477uDjjz/m4MGD1NTUtPslQiQjV03HaKmFmgNG78ajowwgVwlYpYlLwDRDMxMBSImL4umLJ/CPq6ay9K5T2gd/wDUKPsY1Cn6/ATvtRhcNoAH6pcRhsahsnnI//79oU12mD8/Cau2saM5H7vT+I/o7fHYXlGyGxL5wgcn6/nTEFgWn3KfWK56Fuu7HIwshREgVfq1u+4/XpZy2q3HwWv+fabqcJ1zlwtIIWgghhOgxvwNAZ555Jhs2bOBHP/oRAwcOJD09nfT0dNLS0khPP7rURASJLdpThmSmMrAOM4CiAahoaOm0V4BZbDtUC8C4gamcOyGHk/IyPWVf3qw2SB+q1hUmLAProgE0QEyUlX6uhtbFfk4CW+L6YD89kLG+R9L6AO3/Fpqq1XrDe7D2DcCi+v4k9+v5cUJhzLnQfwK01MGSPxu9GyGEaM9d/nWaLm+XP1IFkVbuKqeptX0m7BI9+sRptAsF+1ZDc13P308IIYToxaL8fcHChXIFxjT6jFJj4Mu2q4aOZuBuAn30FLCWNgcNLXYSY/3+axcyWw6pLLZR/XxIWc/IU3/2Fbt6nE6vO3cGUMcBIIABaXEcqmniQFUjEwal+fS2VQ0tfFesAjU9/mC/8DEVSMscDuU7oWgJZI309P0ZMs1TIhYOLBaY8wC8eR58+ypMvRbShxi9KyGEUKXKWqltD/v/aEb2S6J/ahwHq5tYuaucWaNUQKi6oZUN+6oAnQJAGbmQNgSq9sCeZTDyjJ6/pxBCCNFL+f1NPD8/jL6QRbqsUcB802cAxUfbiI2y0tzmoKK+xdQBoK0HVQbQ6P4+9LZxN4I2YU+salcGUNrgTp8yIC2etXurOOBHBtDywnKcTvXBv19KXM/2aLXBwkcgZ5L6/bb/wcJHodXVUNSsTZ+7kjcbhuVDUQEs+gOc/7zROxJCCDiwDhorVUPlgSfq8pbaOPh3v9lHwfZSdwBoxa4yHE7I65NI/9R4XY5F3mxY8zdVBiYBICGEECJgfpeAASxZsoTLLruMadOmUVyspg29+eabLF26VNfNiW6YbRR8SwO0uYIJXgEgi8USFpPAnE4nW10ZQMdk+5ABlGniUfDVXZeAAeS4GkHvr/Q9AOQu/+rJ9C9N/lyYfQ8Uf6t+v/4tKPlerafd7JmsFW7m3K9uN/wDDm82di9CCAGw40t1m5uvepbppKM+QEt2aOVfOpwnNFqW7ZEDA4QQQgjhF78DQP/+978544wziI+PZ+3atTQ3NwNQW1vLo48+qvsGRRfMNgpey/6xxajx414y3JPAzBsAKq1tprKhFasFRvRL6v4FGSYNADkcnjHwnTSBBshxj4L3LQDkdDpZvN31wX6kDmn9oIJAM25vf9+ES+H03+vz/kbIOQGO+RHghAUPG70bIYTw9P8ZoU//H8204VnYrBZ2ldazr6IB8A4A6XSeAJURarFC2TbP+c1ICx+DgnkdP1YwTz0uhBBCmJDfAaCHH36YF154gZdffpno6Gj3/dOmTWPt2rW6bk50I3MEYIHGCqgvM3o37cu/jhg9Hg4ZQFtdDaCHZiUSF+3D1CktAFRZZK5R8HWH1XQyixWS+3f6NC0A5GsT6N3lDRRXNRJjszJlWEb3L/DVqb8DXH9fLDY4LwLKpk65T/35b/tENS4VQgijNFRA8Rq1ztO3X2BqfDQnDFYDQBZtL2VveQN7KxqIslqYkpvZzav9EJ8OA45X612L9HvfQGklzEcGgQrmqfvNPrlSCCFEr+V3AGjbtm3MnHl0b46UlBSqqqr02JPwVUyCp8eLGbKA3AGgo6/6pSdoGUCtodyRX7Tyr9HZPvT/AZVdY40GewvUmOCKpEZrAJ08oMtU/wF+ZgBpY31PGJJOQoyOfZwK5gFOsEaB0975VdVw0mckTPiZWn/1AJh8+p0QIoIVLgCc0HcMpObo/vb5o1xlYNtKWbJTnScmDk4nSe9+f2YqA9NKmL2DQFrwZ/Y96nEhhBDChPwOAPXv35+dO3cedf/SpUvJzc3VZVPCD+4+QNuM3Qd0OAFM484AMnEJmJYBNNqX/j/QfhR8uYlGwbsbQHde/gWeAFBlQysNLW3dvq2W1j9dz7R+7w/Mvys/+gN1OJv1G7DFqqk1O782ejdCiN7KPf5dn+lfR9L6AC3ZUco/Vu0F4OThOmb/aHJnq9tdi1Sps9Hy58L0W9U56/eZEvwRQggRFvwOAF1zzTXcfPPNrFq1CovFwoEDB3j77be54447uO6664KxR9EVrQ9QmQkaQXcwAUzjzgAycwmYawLYKF8zgMCcfYB8aAANKnU/2XWFtrssoDa7gxWF6v/vTL0ae3Z0tbSjq6rhKnUgTL5Krb9+wBxfWIQQvYvD4QlABykAtLe8AasFmtscbDqgMmnfWLGHzzYd1PdAA0+E6ERoKIPDm/R970AULoQN76q1o01lBEvwRwghhMn5HQCaO3cu5513HrNnz6auro6ZM2dy5ZVXcs0113DDDTcEY4+iK1mj1K2pSsCODgBlJKp+UWbNAGqzO9hZUgf4OAFMk5mnbk0VAHKVgHXRAFozwN0HqKnL523YX0VtcxvpCdEcO8CPP5+uOOwdXy3VgkBm6qsUqBm3Q2wKHNoI339g9G6EEL3N4Y1QX6ICJ4On6v72n206yPXvrMVxRJVrRX0L1761Vt8gUFQMDJ2u1kaWgbU1w+f3wJvnQa3Xf5+jFRY8Yti2hBBCCF8ENAb+kUceoaysjNWrV7Ny5UpKS0t56KGH9N6b8IWZRsF3lQFk8ilgRWX1tNgdJMTYGOgaj+4TM2YAVfmWAQSeUfDF3YyC16Z/TRuehdVq6fK5Ppt9d+dXS/PnqsfDXUIGTLtJrRc8DHbz9sASQkQgrfxr2EyIitX1re0OJw/O30xHHc60+x6cvxn7kdGhntD6AO1aqN97+qNkC7x8Cqx41nPfSTdAUrZaL54X/tmrQgghIprPAaCGhgauv/56cnJy6Nu3L1deeSVDhw5l8uTJJCX5MDJbBIdWAlZ7AJqqjd1Lg2sSWUcZQAnmngK25ZCn/MuvAIcZA0B+ZQDFAd2XgC3dqf7fztSz/09vMfVaSOyjpsWtfcPo3QghepMd2vh3/cu/VhdVcLC68+xRJ3CwuonVRRX6HTTP1Qdozwpo9W2AgS6cTlj1Erw0S5WfRSeo+2ffA2c8Aud6BYQioYRZCCFExPI5AHT//ffzt7/9jbPPPpuLL76YL7/8kmuvvTaYexO+iEv1jPou22HsXrpoAm32DKBt7glgfpY3uQNARebp8eJjE2jwbRJYTVMr6/dVATBdr/4/vUlsEsx0ZToVPA4tDcbuRwjROzRVw75Vaq3z+HeAktquS4f9fZ5PskaqCZf2ZtizXL/37UpdCbxzEfzvTmhrUr2UJv2yfQnziNPUfQCxydBSF5q9CSGEEH7yeUbnBx98wKuvvsrFF18MwGWXXcbJJ5+M3W7HZrMFbYPCB1kjVR166VYYOMm4fXTZA0jLAGrF4XDqV0akE60BtM8j4DWpg9T4cnuzGgXvQ9AlqJproalKrX0pAXMFgPZ3EQBaUViO3eEkt0+i+/nCTyf8Alb8Bar2wqoXYMZtRu9ICBHpdhWA0w6ZwyFjmO5v3zc5Ttfn+cRiUWVg699SZWDD9Q9stbPtM/joepXhbIuF0x+CyVerfRzp9IfVhLKKXVBzILj7EkIIIQLkcwbQvn37mDFjhvv3kydPJioqigMH5CRnOLOMgu8iAJSWoJpA2x1Oapu6Hzkeap4R8H4GgGxRnlHwZigD08q/4tLUVchu5PiQAbTUNf59xnAp/wpYVAzMvletlz0FjZWGbkcI0Qvs/FLdBmn61+RhGfRPjaOzyzkWoH9qHJOHHZ0V3CNaGVjhIn3f11tLA3xyO/zjpyr4028sXL0IplzTcfAHICYRzn8JLFbY+D5sksb/QgghzMfnAJDdbicmJqbdfVFRUbS1me/LfK+j9QEyMgDkdHYZAIqNspHkGjlutlHwNU2tFLsCIH6XgIFXGVihjrsKkLsBtG+ZSFoT6EPVTZ026lyyoxSQ8q8eG3ch9D1WlWUsfcro3QghIpnT6TX+/bSgHMJmtXD/OWMAjgoCab+//5wx2PTO+B2Wr24Pb1TlWXo7+J3q9fPNK+r3U6+HK7+GfmO6f+2gE9X0R4CPb5VMICGEEKbjcwmY0+nkF7/4BbGxnikSTU1N/PrXvyYxMdF93wcfyBWPkNMygMoMDAA114DDFQzsoAcQQHpiNHXNbVTUtzAsK7HD5xhhuyv7p39qHKmuTCW/mKkRdLXvE8BApebbrBbaHE5Kapvon9q+xGtfRQO7yxuIslqYmqvzVdzexmqDU3+nriivekFdSU4ZYPSuhBCRqHSrKkuOioOhJwftMGeO7c/zl03kwfmb2zWEzk6N4/5zxnDm2P76HzSpD2QfB4e+UyVXx12kz/s6HGq619e/VyPdk7LhvL/6X2aWfxfs+BIOroePboDL/t151pAQQggRYj4HgK644oqj7rvssst03YwIUNYodVu5R03FiDagT4uW/ROd2OnxMxJi2FfRSKXJGkF7TwALSEaeuq0o0mlHPaCVgPnYi8hmtZCdEkdxVSMHqhqPCgBp07+OH5xGclwAwTHR3sgzYNBU2LdSTYk55ymjdySEiETa+PchJwf9M8GZY/tz2phsVhdVUFLbRN9kVfale+aPt7zZKgBUuFCfAFDNAfjwGiharH4/+odwzjOQeHRGc7ds0XDBS/DiTCj8WmUSTb6q53sUQgghdOBzAOj1118P5j5ETyRmQXy66itStgP6Hxf6PbgngHX+Yck9CcxkJWABTwDTaBlA5SYoAfMzAwhUGVhxVSP7Kxs5YUj7x7TyrxlS/qUPiwUy81QAaO0bMO1G9XtNwTxw2GH23cbtUQgR/nYEt//PkWxWCyflBRAsCVTubFj2tGoE7XT2LMNm80fw35vUAIXoBDjzDzDx8p69Z59RMOcB+Ow38MV9ar9ZwwN/PyGEEEInPvcAEiZmsXiVgW03Zg/u/j+dlwllJLgmgZksAyjgCWCaTFcAqNIEo+C1DCAfewCBdyPo9qN67Q4ny3aq/6/TR0gDaN1oTcOddljwsOf+gnmw8BFVKiaEEIFqroO9K9R6RHD6/xhu8EmqvE2bgBqI5jo14eufl6vgT/8JcM0SOOEKfUq2Jl+j+hW1NcKHV4NdemYKIYQwngSAIkWW1gg6wA9CPdVFA2iNGTOAnE4n27QJYP0DDAClDlaj4Nua1IdRI/nZBBo8AaDiqoZ2928srqa6sZXkuCiOy0nVbYu9Xv5cOPFKtf7+Aziw3hP8mX2PelwIIQK1eynYWyBtsBoBH4mi42DINLUuXOj/6/d/Cy9Mh3VvARaYfhv86kt9s3SsVtVDKDYVitfAkj/r995CCCFEgCQAFCmMHgXvQwAoI9F8GUDFVY3UNrcRbbOQm5UU2JvYotQHbTB2Epi9DWpdE0f8KAEb0EkG0FJX+dfJeVlE2eRHha7O/rOaCAZq2owEf4QQetH6/wyfE9nNh3Nd4+B3+REActih4I/w6ukqazdlIPziY5hzP0TFdP96f6UOhLP/pNYFj0PxWv2PIYQQQvhBvtVFCqNHwfsRAKqobw3FjnyilX/l9UkiJqoH/xzcjaANnARWexCcDrBGQ1I/n182IC0OgANVje3uX7xDNYCW8q8gufht18KpbupKoPawYdsRQoSphY+pLEJwjX/36v9TME89HonyXAGg3Uuhrbn751fugdfPgoUPqxLcYy+Aa5fB0OnB3ee4n8CY89QxP7wGWhq6fYkQQggRLBIAihRaBlBFIdgNCLD4UgKm9QAyUQnYtsM97P+jMUMjaHcD6ByVeu6jgemuErBKTwCovrmNdXsrAZgpDaCDY+P7roXrCv03L8MzE+CrB6GxyqBNCSHCjtWmsggL5qmLEJW71YWA4rWR3Ves77GQ2BdaG2Df6q6f+90/VcnXvpUQkwznvwgXvgbxacHfp8UCP3xSjZUv2w5fPRD8YwohhBCdkABQpEjJgZgkcLQZk4XingLWRRNoE5aAbTmoJoCNCnQCmEYLABmZARRAA2jwlIDVNrdR06SCh6uKymm1OxmckcDgzARdtylo3/PngSqY8DN1f2sDLH0Cnj4OljwhV4qFEN3Ln6t+lix8RE2dAkgZAEv+FNmlpVYr5M5S687KwBqr4N9XwgdXQXMNDJoC1y6F8ReHtjwuIQPOfU6tV78IhQtCd2whhBDCiwSAIoXFAlkj1NqIMjAtAyix83KhjMRowFxNoHvcAFqjjfKuKOrhjnqgaq+69TMAlBATRXqC+n+jZQEt3i7lX0HTUcPn8/4Ks36r1glZ0FQNXz+oMoK+eQXazPNvRghhQvlzIf83sOML9fuqPZEd/NFoZWAdNYLes1xl/Wx8Hyw29efxi089kxhDbcQcmPQrtf7P9dBYacw+AuVdanikSC41FEKICCMBoEjiHgVvYADIhxKw6sZW2uwGj0sHmtvs7CqrB3QsAavYZdwoeHcGkO8NoDWeRtAqALR0pwoAzZQAkP4c9o6/mM26S90/6ZeqPCFtMNQdhk9uh+dOVCUMRv3dEkKYn3eply0m8oM/Cx+Dki1qfWCdJxPZ3gqv/0D9qt6nAj6//Fz9ediiDNsuAKc/pHoG1h6AT+4wdi/+8i419KZd1IjUUkMhhIgwEgCKJFkGNoL2IQCUGh+NxaJ6VFY3Gt8IemdJHXaHk9T4aLJT4nr2ZmmD1RXGtkaoO6TPBv2lBYDS/MsAgvYBoIPVjewsqcNqgZPyJACku9l3d/7FLH8unHKPKk+4YQ2c9SfV46JytypheGE6bPtM/SMSQghNyRZY5MrAsEapMfCdZWtECqsNlj+jsiZxQlGB6sP31DiV/QOqvPbXS2HQiYZu1S0mES54SX1e2PQv2PRvo3fkO+9SQ+3vVkcZrUIIIUxNAkCRxKhR8A67J5W5iwBQlM1KarwqNTJDI2htAtio7GQsPe0FYIv2GgVvUB8gdxNo/zOAclwBoP1VjSxxTf8aPyjN/f9LGCAqBiZfBTevh1N/B7GpUPI9/OOn8NoZsHuZ0TsUQpiBww5vXqCmQGYOh/vKjv6iHom0gESDOmex5M/w3BQ1ETMqDn7yN1VeG9vDDF+9DZwEM25X649vg5oDxu7HH95BoAczJPgjhBBhSAJAkaTPKHVbtiO0pSKNVeqDJ0B8epdPzUgwzyj4rYdUA+hjelr+pTFyEpjT6VUCNtjvl+e4M4CaWOoKAM0YLtk/phCTqL4s3LweTr4FouJh3yr421nw1o/h4AajdyiEMNLbP1ElRbZYuGK+6gnYUbZGJMqfC+N+qtaHNoKjFdKGwI1r4djzjd1bV/LnQv8J0FQFH10fXlmdaUPUrdMOFqsEf4QQIsxIACiSpA1RHwDbGqF6b+iOq5V/xaWqTJgupCdqASATZAC5G0D3cAKYxt0I2oAMoMZKaKlT69Qcv1+e4xoFv7+ywd3/Z7qMfzeXhAw47UG4aZ1qJGqNgp1fwYsz4f1fQNlOo3cohAi18kLYVaDWZ81T0780WhDIYTdmb6FyzpOetcUGN60P6DwYUrZouOBllalUuEA1+ze71iaYfzN8eLXnPqdD9akTQggRNiQAFElsUSr9G0JbBuZD/x9NeoL5AkCj9M4AqjAgA0jL/knIguh4v1+u9QD6bn81FfUtJMbYOH5wmo4bFLpJ6Q8/fAJu+AbG/QSwwPcfwnOT4b83QnWx0TsUQoSCw6G+kDvbYNhMmHjF0c/Jn6v6jkWyFa7x6rZolZWy5E/G7sdXfUbCnAfV+ov7VPa2WZUXwqtzYM3f1O+HTocx56r1N69EdpaZEEJEGAkARRqtDMykASBtFLzRPYDK65oprW0GYFQ/vQNABoyC70EDaIB+KbEA2B0qDX1qbgbRNvnxYGoZufDjV1SD05Fnqi8+a9+AZ46Hz+/xTMQRQkSmtX+D3UsgOgHOeUaVfvU23k2Iw7H30eSrIXeWytz+4Go1wcxsNn8EL81SJXYAx10Mv/hE/VlbXJ8TwunPXAghejn5hhdpTB4AMksJ2DZX9s/gjAQSY3UaC5vhVQIW6nr+HjSA/mzTQc5/bnm7+77ZXclnmw7qsTMRbNlj4dL31JjjISeDvRlWPAtPHQeLHofmWqN3KITQW/V++OJ3an3KfZAxzNj9GKGjCVTh1vvIaoVz/6pK6A+sVY2szaKtBf73G/jn5dBcAyk5cNKNcMGL6vE+o1xZqKjPP5FeaiiEEBFCAkCRRhsFX2bOAJDWBLrS4ACQu/+PXuVf4BoFb4XWBqgN8Sh4dwDIvwbQn206yLVvreVQTVO7+2ua2rj2rbUSBAong6eqq7I/+zdkHwcttbDoUXh6Aqx8Htqajd6hEEIPTid8fKv6Nz5wMky5xugdGcNh73gCVbj1PkrNgbNcgZ+CeVC8xtj9AFTtg9d/AKueV7+fdiPcvAHOeLj98/LvUn2XKgph+Kmh36cQQgi/SQAo0rhHwW8PXRaKOwCU0e1T3RlABpeAaRPAdA0ARcUYNwq+yv8MILvDyYPzN9PV35IH5292l4WJMGCxwIg5cHUBXPiauirbUAaf/Qb+cgL84xJY+IeOX1swDxY+Ftr9CiH8990/YccXYIuBc58Fq83oHRlj9t2dT6AKt95H4y6EYy9QpbwfXA0tDcbtZfsX8OIMKP5WZSZd/A6c/nDHQz4y82D8JWq98JHQ7lMIIURAJAAUaTLzVBZKc3XoslC0XiNhlAG0Te8JYBp3H6AQB4DcI+B9DwCtLqrgYHVTp487gYPVTawukl4yYcdqhbE/hutXwTlPQ/IAlSW27VMoeAze/7/2AWKtlKK3fpEUIlzUlcBnd6l1/lxP2bcIbxYLnP1nSO4P5Tvhq/tDvwd7G3z1ILzzEzVZdMDxcM1iGH1216/Lv1NNpdy1CHYvC8lWhRBCBE4CQJEmKtYThAhVGVggPYAMzACyO5xsO6zzBDCNUZPAAmgCXVLbefAnkOcJE7JFwwm/gJvWqiu48enq/u8/gCeOgQPrO+6jIYQwp0/vVF/Os8fBybcYvRuhp4QMldEFsPolNR4+VGoPwRvnwtIn1O9PvEr1lUsf2v1r04fC8T9X64WPhL4HohBCCL9IACgSZYW4EbRfU8C0DCDjJl3sKa+nqdVBbJSVoZmJ+r65dyPoUGlrhjpXtleq7wGgvslxuj5PmFh0vKeHw8y5YI2G2oPwUr4Ef4QIF1vmw+b/qJ4r5z7XcUmOCG/D56jgC8B/rgvNNMeixfDCDNizFGKSVPnw2X9SFxR9NfMOVZK4ZxkUFQRvr0IIIXpMAkCRKNSTwAIIANU1t9HcZkyDRq38a1R2MjarzmNztQyg8hAGgGqK1W1UvE//DzSTh2XQPzWOzv4ELED/1DgmD+u+t5MIE3GpcMo9cNsWz30WmwR/hDC7xkr45Ha1Pvlm6D/e2P2I4Dnt95A5XAXpP70jeMdxOKDgjyrzp74E+h4LVy9S5cP+Sh0IJ/yfWi+QLCAhhDAzCQBFopAHgHzvAZQSF+UOulQ1GJMFtEULAPXTufwL2vcACtUHIO8G0BbfA1o2q4X7zxkDcFQQSPv9/eeM0T9IJoy35nXP2mmHrx8ybi9CiO59fg/UHVaTPvPvMno3IphiEuD8l1RwftO/YeO/9D9GfTm8fSEsfBicDphwGVz5FWSNCPw9Z9wGUXGwfzXs/Eq/vQohhNCVBIAikRYACkUPIHurajgNPgWALBYL6a5G0BUGNYLepk0A07sBNED6ENco+HrVrDMUAmgArTlzbH+ev2wi2anty7yyU+N4/rKJnDm2vx47FGai9fzJ/w2kD1P3LfmTul8IYT47v4L1bwMW+NGzEC1luRFv4Akw8061/uQ2qDmg33vvXaWmfBV+rTKHz30OzntOBZ56IjkbTrxSraUXLf3cyAAAaIpJREFUkBBCmFaU0RsQQZA1Ut3Wl6rsHB/GswdMy/6xWCEuzaeXZCRGU1bXbNgksK3aBDC9G0CDqplPHQhVe1Uj6OR++h/jSAE0gPZ25tj+nDYmm9VFFZTUNtE3WZV9SeZPBDqy4XNGLnx4tbpqq43wlXIwIcyjuRbm36LWU34Ng6cYuh0RQjPvgB2fw4F1qh/QZR+oCY+BcjphxXNqwpijTZWZXfQG9DtWvz2ffAt8+7ra87ZPu58gJoQQIuQkAygSxSR6mgEHuwxM6/8Tn+HzBxN3BpABk8Dqm9vYW9EABCkABKFvBF29V9360QD6SDarhZPyMjl3Qg4n5WVK8CdSOeztGz6Pu1A1jW9rgqEz1ONCCPP46kGo3gdpQ+DU+4zejQglW7QqBYuKg10L4ZtXAn+vxip47zL44h4V/Dn2AtXvR8/gD0BSH5hytVovfFT1GRJCCGEqEgCKVKEqA/OjAbTGMwks9AGg7YdrcTohKymWzCQ/Jlz4w7sPUCi4S8ACDwCJXmL23e0zfKw2mP1btT6wHqZcY8i2hBAd2LMcvnlZrX/0jLq4I3qXPiPhNFePti9/B6Xb/X+PA+vgxZmw9WM1qeusP6lJX7FBugg27SaISYbDm2DLf4NzDCGEEAGTAFCkCtUo+AACQOmJWg+g0DeB1iaAHdM/SB98wGsSWGHwjuGtBz2AhOCYH0H2OGiphWVPGb0bIQRAayN8dINaT7wccmcZuh1hoBOvhNzZ0NaoSnbtPn52cjrhm1fh1dOhag+kDYZffg6Tr/JrYITfEjLgpOvUetFjklkqhBAmIwGgSBWqSWANZerWjz5DGa4SsEoDSsC2BnMCmCYzhCVgTqcEgETPWK0w+161XvUS1B42dj9CCPXFuaIQkvvD6Q8bvRthJKsVzvsrxKWqbJ7Ff+r+Nc118MFVqoG0vQVGnQXXLIacicHfL8DU69R+S7fCpg9Cc0whhBA+kQBQpApZAMj3EfAaTwZQ6ANAWw4GcQKYJpSj4OvLVP8WLJCSE9xjicg18gzImaSuMC99wujdCNG7Fa+F5X9R6x8+qb5Ii94tZQCc7frZvPiPsH9N5889vBleng0b31ej5E97CC5+B+LTQ7NXgPg0mHajWi96DOxtoTu2EEKILkkAKFJpk8Bq9qspIsESUA+gaCD0GUBOp5Nth4M4AUyTNgSwQEudmsQWTFoD6ORsiIoJ7rFE5LJY4BRXFtC3r3myyoQIxMLH1MS5jhTMU4+LjrW1qNIvpwPGXgijfmD0joRZjLsQxv4YnHZVCtbScPRz1v8DXj4FyrZD8gD4v0/h5JuCW/LVmSm/VgNCKgrhu/dCf3whhBAdkgBQpErIgMS+al0WQNNAXwXSA8hVAlZeF9oA0OGaZqoaWrFZLQzvmxS8A0XHeRoyB7sMTBpAC73kzoIh01W5wOI/Gr0bEc6sNlj4yNFBoIJ56n6rzZh9hYOlT0LJ9+qc+oPHjd6NMJuz/gQxSVC+UzWF1mg9o/7za5XJmXcK/HoJDJ5q3F5jk2H6LWpd8LjvvYuEEEIElQSAIpm7DMxcASD3FLAQZwBtPaTKv4ZlJRIXHeQvIBnD1G2wG0FL/x+hF+8soHVvhW6KnYg8+XNh9j3tg0Ba8Gf2Pe0n0QmPw5s9wdcfzIPELGP3I8wnIQNG/1Ctv3kZdn6lPme8MgfWvanuHzYTfvYvc/z9OfFKSOyjmlCve8vo3QghhEACQJHNHQDaGrxj9CADqKK+BWewe+R4cTeADmb5lyZUjaCr9qlbCQAJPQw5CYbPAUdb5yU8Qvgify7MulsFfX6fKcGf7jjs8N8bwNGqGvaO/bHROxJmdcGLqmcbwHs/hxfz1ch1gAmXwhXzzZNlF5MI029T68V/grZmY/cjhBBCAkARTRsFH9QSMP+bQGsZQM1tDhpbQzce1D0CPhQBIHcj6GBnALkCQGmDg3sc0XvMvkfdfvde8JvIi8im/Rx0tIEtRoI/XVn5VyheA7GpqtmvET1bRPi4Yr7qr9PaAC2uPo/TboLznjd2Xx2Z9Es1za5mP6x9w+jdCCFErycBoEgWiklg7gwg38fAJ8TYiIlSf/VCOQlMmwA2KjuIE8A03pPAgqlaMoCEznImqhIDp0NNbxEiEA4HfP5bz+/tLZJV1pnyQljgGvV+xsOQ0t/Y/Qjzi0mAyz8CXIFCWwyc/pChW+pUdBzMuF2tF/9J9SsSQghhGAkARTItAFRZBK1N+r9/S4O6+gR+ZQBZLBYyXGVglfWhaQrYandQWFoHBHkCmCZDKwErCu4oeGkCLYJh9m8BC3z/IRzaaPRuRDj64Or2UxAHT+u4MXRv53DAf2+CtibViP34nxu9IxEutn8GOFXwx+wB1omXq88pdYfUpEkhhBCGkQBQJEvqB3Gp6kp+MEqRGl3lX9ZoNe3BD+muMrCKEDWC3lVaT6vdSVJsFAPT44N/wPShgAWaa6C+LDjHaGnwZGBJBpDQU79jYewFar3wUWP3IsLPosdh0/tqrQXD7c1HN4YWsOZ12LMUohPgnKel9Ev4xrup+n2l5v+3FRULM+9U66VPQku9sfsRQoheTAJAkcxi8fQBCkYjaO8G0H5+aM1IjAagMkQlYNoEsFHZyVhC8QE7Og5SctQ6WGVgWvZPTLIK9Amhp1l3g8UK2z6F/WuM3o0IJ5VF6tYWCxe+qtYH1sHkq9UXVUfoer+ZWvV++PJ+tT71fteFAyG60dFEvY4m75nNhEvV3/H6Ulj9ktG7EUKIXksCQJEumKPgA5gApslIjAVC1wNImwAWkvIvTWaQ+wC5G0APkqvGQn9ZI2D8JWq9wKS9JYQ51R5StxN/DgOOh8wRKhN1zzLXF9W7jd2fGTidMP8W1cB30BQVHBPCFw57xxP1tCCQWQOstmjI/41aL3sammqM3Y8QQvRSEgCKdMEcBe+eAOZ7A2hNRoIrAyhEJWBbXQ2gQxoACvYkMGkALYItfy5Yo2DXQti91OjdiHBwYJ36+2KxqalEALn56nbXIsO2ZTrfvQc7v1RZUj96FqzycUz4aPbdnU/UM3uAddxPIHM4NFbCqheM3o0QQvRK8okj0vUZrW6DMQq+BxlA7h5AIcoA0kbAj+4fgglgGncj6CCXgEkDaBEs6UNV806ABY8Et6G5iAxLnlC34y6E9CFqPUwLABUYsyezqSuBz1yZELPugj4jjd2PEKFii1LlxQDLn1WBICGEECElAaBIl+X6YFm2A+xt+r53j0rAXFPAQpABVN3QyoFqNQVtZD8DMoDKg5UBpAWAJANIBNHMO1WWwt7lULjA6N0IMyvdDlvmq/X0Wz33D50OWKBsG9QcNGRrpvLpHeqLb/ZxniwpIXqLYy+APsdAczWs+KvRuxFCiF5HAkCRLnWQmi7iaIXK3fq+d08ygBJClwG07bDK/slJiyc1Pjrox3Nzl4AFaRR8lVYCJhlAIohSBsCJV6r1goclC0h0btlTgBNGnQ19j/Hcn5AB/cerddFiI3ZmHpv/C5s/UqWV5z6n+qII0ZtYrZ4ytZXPe9oJCCGECAnTBIAee+wxLBYLt9xyi/s+p9PJAw88wIABA4iPj2fWrFl8//33xm0yHFmtqpkrqKuvetICQIlZfr/UnQFU36rnjjrkPQEspDKGqdvm6uB8wPFuAi1EME2/VQWSD6yFbf8zejfCjKr2qb42ADNuO/pxrQ9QUS8uA2uogE9uV+uTb4H+xxm6HSEMM/ocyB6nmqAve9ro3QghRK9iigDQN998w0svvcRxx7X/MDRv3jyeeOIJnn32Wb755huys7M57bTTqK2tNWinYSpYo+D1yAAKQQmYIRPAAKLjvUbB61wG5rBDTbFaSwmYCLakPjDl12q98BFwOIzdjzCf5X8BRxsMmwkDJx39eO4sdburoPdmkX1+D9SXqHNyZ018hegNrFY1sQzUSPi6EmP3I4QQvYjhAaC6ujp+9rOf8fLLL5Oenu6+3+l08tRTT3HPPfdwwQUXMHbsWP7+97/T0NDAO++8Y+COw1CwRsHXawGgAKaAuTOAWnAG+cuANgEs5BlA4FUGpnMj6LrD6suWxQbJ/fV9byE6Mu1GiE2Bw5tg83+M3k14WPgYFMzr+LGCeerxSFBXCmvfUOvpHWT/AAyaCrYYqNkfvL5oZrbjK9jwDmCBc5+FqFijdySEsUaeCTknQGsDLH3K6N0IIUSvYXgA6Prrr+fss89mzpw57e4vKiri0KFDnH766e77YmNjyc/PZ/ny5Z2+X3NzMzU1Ne1+9XrBGgXfgwygNNcY+DaHk9pmnZtTe3E4nO4JYMeEcgKYJlgBIK0BdEoOWG36vrcQHUnIgJNuUOuFj+rfVD4SWW0qY+rIIFDBPHV/pPzbXfU8tDXCgImeTJ8jxSTAoClqXbQoVDszh+Za+PgWtZ56LQyabOh2hDAFiwVm/1atv31VGsQLIUSIGBoAevfdd1m7di2PPXb0VdBDhw4B0K9fv3b39+vXz/1YRx577DFSU1PdvwYNkv4onlHwO/Qr3XA6exQAiou2kRijvvxUBrERdHFVI/UtdmJsVoZlJQbtOJ0K1iSwqr3qVsq/RChNvRbi06F8B2x83+jdmF/+XFXmsPAR+OpBOPidJ/gz+57IKANqqobVr6j1jNvUl7rO9NZx8F89oHq2pQ+FU+41ejdCmEfeqSow3NYES/5s9G6EEKJXMCwAtG/fPm6++Wbeeust4uLiOn2e5YgPk06n86j7vN19991UV1e7f+3bt0+3PYet9GFgjYbWepV+r4fmWjVZDCDe/xIwgHRXGVh5EANAW1zlX3l9k4i2GfDXPTNP3QYrA0gaQItQiktRzWsBFj0G9uA3cQ97+XNhxu2w9Al4cUZkBX8AvnlVNbrPGqWmf3VFawS9e0nv6SO1exl84wqQnfMMxBhwIUIIs7JYPL2A1v7dM91UCCFE0BgWAFqzZg0lJSWccMIJREVFERUVRUFBAc888wxRUVHuzJ8js31KSkqOygryFhsbS0pKSrtfvZ4tCjKHq7VefYC07J/oBJXaHwDvPkDB4i7/MqL/D3iVgBXq2/hUmwAmGUAi1CZfBYl9oWoPrHvL6N2Yn9MJZUf83J16rTF70VtrI6z8q1pPv1U1du3KgIkQkwyNlXDou+Dvz2itjfDfG9V64hWeAJgQwiM3H4bOAHsLLPmT0bsRQoiIZ1gA6NRTT2Xjxo2sX7/e/WvSpEn87Gc/Y/369eTm5pKdnc2XX37pfk1LSwsFBQVMmzbNqG2Hrz4j1a1efYC0seYBlH9p3JPAghgA0iaAGdIAGlT2FagyicZK/d5XywBKlQwgEWIxiSqjBWDxH6G1ydj9mN2K52DL/Pb3vXmBMXvR27q3oL4UUgfDuAu7f74tCoaerNaROA7+yKbfCx9Vwf/kAZCYFTlNv4XQm5YFtO4tqCgydi9CCBHhDAsAJScnM3bs2Ha/EhMTyczMZOzYsVgsFm655RYeffRRPvzwQzZt2sQvfvELEhISuPTSS43advjSRsGXbdPn/RoCnwCmcWcABXEU/NZDqgRstBENoEFlRyUPUGs9y8AkACSMNOn/IGUg1BTDmteN3o157VkBX7h6vow4A372b7Xevxo+7mRaVriwt8KyZ9T65JvAFu3b69zj4BcFY1fG8m76XbwGVjyr7h86XfU3iZSm30LobchJkHeKmm66+I9G70YIISKa4VPAujJ37lxuueUWrrvuOiZNmkRxcTFffPEFyckGZXOEM71HwfegAbTGkwEUnD4iTa12isrqARhtVAYQBKcRdJWUgAkDRcVC/p1qveTP0FJv7H7MqK4E3r4QcELfMXDpezBiDhx7vnr821dh0R8M3WKPbPo3VO+FxD5w/GW+v05rBL1nBbQ1B2dvRvFu+v3OT8HpgL7HwsZ/RlbfJyGCYbYrWL7hH1C209i9CCFEBDNVAGjRokU89dRT7t9bLBYeeOABDh48SFNTEwUFBYwdO9a4DYYz71HwevSi0SEAlJGorhgHqwfQzpI6HE5IT4imb3JsUI7hkwxXGZheGUBN1arpKkgASBhnws/UVKP6Ulj9ktG7MRd7G/zrl9BSp35G/upLz3SsM/8Asa6MxP3fGrfHnnA4YMkTaj31WoiO9/21fY9RPaTaGmH/N8HZn5Hy58Lw09S/C4CS7yX4I4QvBp4AI89UgdOCMA6OCyGEyZkqACSCKHM4WKzQVOX5YNoTemQAuUrAKoJUAqZNABuVndzl5Lig03sSmFb+FZ8OsUn6vKcQ/rJFw6y71XrpUyowKZSFj6hJV9GJ8H//a//vNDkbTv2dWu9bBTUHjdljT2z7VJUTx6bAiVf691qLBYbNVOtIHQfv3e/NFiPBHyF8Nfu36nbjv6Bki7F7EUKICCUBoN4iOh7Shqi1Ho2g9cgASgjuFDCtAfTobIMnwXlPAtOD9P8RZjHuJ5A1UgWWVz5v9G7MYdv/1Mh3gHP/4sm+9Dbpl2oiVnMNfH53aPfXU06n57/vxCshLtX/99CmYUViI+iaA1DsyuyyRavJRt6NoYUQnes/Hkb/EHDCImmaLoQQwSABoN6kz2h1W6pDI2gdm0AHKwPIPQK+v8E9ozL0zgDS+v9IAEgYzGrzXLFd8ZxnOmBvVVEEH16j1pOvgbE/7vh5Vhuc8zRYbPD9h7Djq9DtsaeKClSD46g4mHpdYO+h9QHa/y001ei3NzOYf4u6TcmB+8o8PYEkCCSEb2b/FrDA5o/g4HdG70YIISKOBIB6E/coeD0CQD0fA++eAha0DCCtBMzoDCBXD6DGSn2+IEsDaGEmx5wL/capbJblzxi9G+O0NsH7V6hSuIEnwukPd/38/sep/jkAn9wGLQ3B36MetN4/Ey+HpD6BvUf6EEgfBk477Fmu396MVjAPdnyu1lNcgUDvxtASBBKie/2O9TTLD+dG+UIIYVISAOpN9BwFr2MPoKrGVuwOHRpTeymtbaasrgWLBUb2M7hPTkwiJGWrdUVRz99PKwFLkwwgYQJWK5xyj1qvelFNv+qN/jcXDm5QPxN/8jeIiun+NbPuhpSBULUHFodBcGD/GpUBZI2CaTf27L0isQysuQ5w9Zsb/UPP/VoQyGE3ZFtChJ1Zd6u+lds+geK1Ru9GCCEiigSAepOglIAFHgBKi1dTwJxOqG7UdxS8Vv41JCOBhJgoXd87IHo2gq6WDCBhMiPPhJwToLUBlj5p9G5Cb/07sPbvgAV+/Irv/zZjk+AsV+Bn+V/g8OagbVEXWu+fcRdB2uCevZdWBhZJjaD7HgM41eh37We+Jn8uzA6zfk9CGKXPSPVzBmDho8buRQghIowEgHqTrBHqtu4wNFYF/j4OBzT2vAQsymYl1RUEqtC5DEwr/zK8AbTGPQpeh0bQ7ibQPfwCJoReLBY45V61/uZVqC42dj+hdGgTfHyrWs+6G/JO8e/1o8+GUWeDo029j8Oh/x71ULIVtn4MWGD6LT1/P20SWMn3UKfDZEoz2DJf3R5zjrH7ECIS5M9VfdJ2fgn7Vhu9GyGEiBgSAOpN4lJUY0qAsu2Bv09TFThdX1J6EAACrz5AOjeC1iaAjco2uAG0xj0JrIcZQPZWqHWNjZYMIGEmubNhyHSwN8PiPxq9m9BoqoZ//hzammD4HJh5Z2Dvc9Y8NTJ+30pY96a+e9SLltl1zA87nmzmr8Qs1TsKIqMMrKUeCr9W62N+2PVzhRDdy8yDCZeq9YJueqoJIYTwmQSAepssrRF0D0bBa42MY1PVmNseSE8ITgaQaSaAafSaBFZzQAXfbDGQGGADViGCwWLx9AJa96Y+/a7MzOmEj65X/6ZTB8EFL6t+SIFIHeiZpvbl78yXEVO5Bza+r9bTb9PvfSOpD9DOr1UgMG0I9Btr9G6EiAwz7wRrtPoZsXup0bsRQoiIIAGg3kaPPkANZeq2ByPgNcGYBNZmd7D9sJYBZJYSMFcGUHkPS8Dc5V8DA/+yKUSwDJkGeaeqcqZIn3i04llV8mONhp/8vec/D6f8GrLHqQzLL+7VZYu6Wf4XNbErdxbkTNTvfXNnqdtdi/R7T6N4l39ZLMbuRYhIkT4EJv5crRc8ogLvQgghekS+QfY2eoyC16EBtCY9QQWAKnQsAdtd3kBzm4P4aBuDMxJ0e98e0QJAjRVqHHygpAG0MDstC+i7d6G0B6WmZrZnOXx5v1qf+RgMPKHn72mLgh8+DVjUn51ZmiPXlXjK0mbcru97Dz5JTRSr2hveGWNtLbDdNf5d+v8Ioa8Zd4AtFvYuj4xgsRBCGEwCQL2NlgHUk1HwOgaAgpEBpJV/jcxOxmY1yZXY2CRI6qfWPSkDcweApAG0MKmcE1RTY6cDFj1m9G70V3sY3v8/lREz7idw4pX6vffAE+DEX6n1J7dBW7N+7x2olX9VpU05k2DoDH3fOzYJBp6o1uFcBrZ7CTRXQ2JfGDjZ6N0IEVlSc2DS/6n1QskCEkKInpIAUG+T5WreWbVXNa0MhJ4ZQK4AUEW9fmPg3RPA+pmk/4/G3Qi6B1e6vUvAhDArrZ/N9x+oKVmRwt4G//4V1B1SwfRznta/3OfU36lgcflOT+NlozRWwepX1HrG7cEpbYqEcfBa+dfos6U0V4hgmH4bRMXD/m9gx5dG70YIIcKafFLpbRIzPYGbsh2BvYc7AKRDDyCtBKxevyvd2gSw0WZpAK3RoxF0lZSAiTCQPRaOvUCtFz5q7F70tPBhle0RkwQXvQkxifofIy5VlZUBLPkzlO3U/xi++uYVaKmFPsfAyDODcwx3I+jF4HAE5xjB5LDD1k/UWqZ/CREc374G/Y9T6yOzgArmwcIIzDYVQoggkQBQb9TTRtDaFDA9M4Aa9M8AMs0IeE3GMHXbk0bQWgZQ2qCe70eIYJp1N1issO0TKF5j9G56buunnoycH/3F008tGI69QI2Vt7fAJ7caU/LQ0gArn1fr6bcGL7MlZxJEJ6jhAiWbg3OMYNr/DdSXqKmYQ2cavRshIpPVBvtWqab7B9d7gq4F81RAyGozdHtCCBFOJADUG2mj4APtA6RrDyA1Bl6vHkB1zW3sq2gEYLRZJoBp3CVgAWYAOZ1eJWASABIm12ckHHexWi94xNi99FRFEXz4a7We8msYe0Fwj2exwFl/gqg4lRnz3T+De7yOrHtTBWXSBsPYHwfvOFExMORktQ7HBq9a+dfIM9R/ixBCf/lzYfY94HBdLFz4KCx6XAV/Zt+jHhdCCOETCQD1Rj3OANJ/CpheASCtAXTf5Fh3g2nTyNRKwALMAGqshFZX36aUHH32JEQwzbpLTXkq/FpNzgpHrY3wz5+rJr8DJ8NpD4XmuBnDPF9qPv+tJ/MyFOytavQ7wMk3qwllweQuAwuzPkBOJ2z9WK2l/EuI4Mqfq7IRAUq+h0WPSvBHCCECIAGg3qino+CDMAWstrmNlrae93/Y5u7/Y7LsH/BkADWUq+aq/tImgCX2heg43bYlRNCkD4WJl6v1gofDc3rLp3fCoY3q591P/hbaLI+TblQB+4Yy+Or+0B134/vq501iX5hwWfCPpzWC3rNcBZ/CxeFNULlbZWoNn2P0boSIfHMeAItXudeEnxm2FSGECFcSAOqNtAygil3QFkDmjY4BoJS4aPeo9qqGnmcBuSeAma3/D0BssvpCBVAZwCQwaQAtwtGMO8AWC3uWhV+Jz7q3VCkUFvjxq2occShFxcAPn1LrtW/AnhXBP6bD4el1dNL1oQk29xurzictdeHVL2qLK/sn79TgNAQXQrRXMA+cdsA1kfDvPwzPCwtCCGEgCQD1Rsn9ISZZnUT9LUeyt0JTtVrrEACyWi2kJ6g+QBV6BIAOujKAzBgAAk8WUCCNoKUBtAhHqTlw4q/UesFD4fNh/eB38Mntaj37Hsibbcw+hpwEx/9crT++NbCgvT+2fgxl29U0skm/DO6xNFYrDJ2h1uE0Dl7Kv4QIHa3h8+x74PrVKhOoYhe8K1lAQgjhDwkA9UYWC/QZpdb+loE1VmpvAvFpumwn3T0KvmdfbJxOp1cGkAlLwMCrEXQAGUBaCZg0gBbhZvqtatJT8RrY/pnRu+leYxX883Joa4IRp8OM243dz2m/VwH30i2w4tngHcfpVKPnASZfDXEh/Dkabn2AKnapEjCLDUaeafRuhIhs3sGf/LmqlcEcV1nstk/g83uM3Z8QQoQRCQD1VoEGgLTyr/h03cZuaqPgK+t71vvhYHUTNU1t2KwW8vqaNB0/sweTwCQAJMJVUl/IPk6tFzyiyoy8FcyDhY+Ffl8dcTrho+tVmWbqYDj/xeCNQPdVQgac7pqkVjBP9Z0Jhl0L1YjlqHg17SyUtD5A+1ZDS31ojx0Irfxr6HT1/0cIETwO+9ENn0+6AQaeqNZbPw6f7FIhhDCYBIB6q0BHwevY/0eToWUA9bAETGsAndcnkdgofYJTunNnAPWgBEx6AIlwNOQkdXt4I2z5yHO/dmVXp4Byjy1/Rn2ZsMXARX83z5f78RerMqm2RvjkjuB82VnyhLo94QpIzNL//buSkasCbo7W0PQ66il3+dc5xu5DiN5g9t1HT/uy2uC851UT9srdsPbvhmxNCCHCjQSAeqtAR8EHIQDkyQDqWQBoi6v8a5RZy7/AKwAUQAaQNIEW4WzOA54+L5/crq7oHpnWb7Tdy+CrB9X6zD9AzkRj9+PNYoEfPqkCUzu/hM3/0ff9930Du5eANQqm3ajve/vCYoHcmWpdtCj0x/dH7SHYt0qtR59t7F6E6M2yRsAp96r15/d6PicJIYTolASAeittFHzZDvVFzFfByABKdDWB7mEAyD0C3qwNoMETAKovhaYa31/X2gT1JWqdNlj/fQkRChe/ra7WNpTDQ1kq+JPfwZVdI9Qegn/9n2qOf9xPQ9cA2R9ZI1Q/JYD//cbTkF8PS13ZP8ddbFyQedgsdWv2RtBbP1G3OZMgZYCxexGit5t6HQyaAi218N8bpBRMCCG6IQGg3iptiPoiZm+Gqj2+v04LACXqmAHkKgGr7GEJmOkngIGarJPgKq3wJwuopljdRieo/ktChKO4VDj1d2rtdPUBWv0ifHgtbJlvXO8Xexv865dQdxj6HKMybSwWY/bSnem3QUYe1B2CBQ/r856HN8O2TwELTL9Fn/cMxDBXBtChjdBQYdw+uiPTv4QwD6sNzv2r+ky7axGsed3oHYlwtfAxlZncETP1KhSihyQA1FtZbZA5Qq39KQPTPpTrmgHU8ylgLW0OCkvrABjd38QlYACZeerWnwCQdwNos34xFcIXzerfKRbX6aexAja8A+9dBvNy4Z2fwpq/Q11J6Pa04PewZxnEJMNP34QYkzaRB4iOg7Ndk7pWv6wmq/XU0ifV7ZgfqSwjoyT3UwE4nFC02Lh9dKWx0rO30dL/RwhTyBruubjwxX1Q6ceFTSE0VpvKTD4yCGS2XoVCH7044CcBoN4skElg9WXqNhg9gHqQAVRYWkebw0lyXBQDUuP02lpwBNIIWhpAi0hQMA8WPap6/txfqcq/AAZOVlmJbU1qTPz8m+BPI+GV02DpU6pUNVi2fgLLnlbrc581NgDiq7zZqkwNJ8y/RWUwBaqiCDb9W62n36bH7npGGwe/a5Gh2+jU9i/A0aYCVVnDjd6NEEIz5dcw+CRoqVOlYEdOmxSiO/lz1ecT7yCQ2XoVCv304oCfBIB6s0ACQEGcAtaTMfBbXQ2gR2cnYzF7how7AFTk+2ukAbQIdx19iJr9G/X7/athws/g2uUw+14YcDzgVPd/dT88Own+Mgm+/B3sXeVf37KuVOxS5Weg+kgce54+7xsKpz8CcWlw6DtVRheo5c+ovkd5p8KACXrtLnC5s9RtkUn7AG2dr26l/EsIc7Ha4NznICpeZemtec3oHYlw5B0EejBDgj+RzPv/tVZS30sCfhIA6s20AJA/o+CD0gS65yVgW90NoE1e/gWBTQLTMoDSBum/HyFCwWHv+ISqnYCdDuh3LOTfCVcvgls3w1l/grxTwBoN5TtUps5rp8OfR8FHN8C2/0FrY2D7aW2E9y6H5mrVQPS03/f4PzGkkvrAaa6JZQse8fyM8EftYVj3tlrPMEH2D8CQk8FiUz8fzTbRp6UBdnyl1jL+XQjzycyDOfer9Re/U+PhhfDXxCvUrdOuzkcRHAjo9fLnwtTrYfEf4cH0XhH8AQkA9W5ZWgbQdt+nJgShB5BWAtbYaqexJbAr+1oD6FFmbgCt0QJA5f6UgHn1ABIiHM3uYtpX/lz1uLfUHJh8Ffz8Q5hbCBe+BmMvhNhUNUVv3Zvwj4tV36B3fwbr34H6ct/38+kdcHijasr+k7+BLTrg/zTDHH85DJoKrfXwv7v8f/3K59QggEFTVODFDOJSIGeiWpstC6hwAbQ1QupgyD7O6N0IIToy+RoYPE39XPxISsGEn1oa4KV8z++ddlj0B+P2I4LP4apAcTrAFhPxwR+QAFDvlpGrItsttVBzwLfXuDOAMnTbRmKMjRib+qtYEWAfIG0E/DH9wygAVF8CzbW+vUYCQKI3i0uFsT+GC1+FO3fCz/8Dk6+GlIHQ2qCmMv3nWvjTcHj9LFj+rCfDrqMmf2vfhHVvARYYPid8R3lbrWpimTVK/Rls/dT31zZWwjevqvX028zVXH6Y1gfIZAGgLVr51znm+vMSQnhYrXDec2pq6u4l8O2rRu9IhAuHQwV/ag+qqXJxaer+RV00Cxbhrb4MvnWVi1qjwN7SK/5fSwCoN4uK8UykKt3a/fNbG9UVFdA1A8hisZCeqK6+VwZQBlbV0MKhmiYARvYLgwBQfJrnz8+XMjCHA6pdY+ClB5Do7aJiVBPks/4It26CaxZD/l3Qb5y6erNnGXxxDzxzPDw3FXYvVim9ix5Xrz/4ncr+AcDp+RkYrvqNgZNuUOtP7/RMWevO6ldUs9S+x8LIM4K3v0BojaCLCnzPTg02eyts/59aS/8fIcwtIxfmPKDWX/7Ov56Lovd6/Uwo264ujv/8QzjuInV/3zEdNwsW4e/9X6jBDknZcF/Z0U3AI5QEgHq7rJHqtmx798/Vyr+sURCrb6+d9ITA+wBp/X8GpseTHBcmZRz+9AGqL1VlGhZr+GYqCBEMFgv0Hw+zfwvXLoWbv4MzH4dhM9UHuNItsGe5eu6iR+Gl2fDPn6tpYwCzfhsZqb75d0HaYKjZr65UdqelHlb+Va1nmCz7B9RUuKg4qDvs28WJUNi9FJqqVcngoClG70YI0Z0Tr4Ih01WWqJSCie58+zrsW6XW5z0PQ6bBhEvV78sLVaasXgMohDkseFhlCQKc+Zj6LNTRJLgIJAGg3q7PaHXryyQw7wbQOn9hyOjBKPitBz0TwMJGhivrwJcAkNbcNbl/ePYpESJU0ofA1F/DFfNV36ALXoYx50FMknr8wFpPU9Dpt8KsAPrmmFFMApz1Z7Ve+Twc2tj189e+AY0VkD5U/fmYTXScGucM5ikD08q/Rp8d0aNhhYgYViuc+yxEJ8KepfDNy0bvSJjVzq/hk9vVOv83MP6nat1/gsr+sTeriyxH9ioU4a14nbpNHwZjzvXcrwWBIjjgJwGg3s6fUfBBmACmSe/BJLBth8NoApjG3QjalwCQjIAXwm/x6Sp9+6K/w9xd8LN/qyw6UFPFtPKASDHydPUBxmmH+bd0/sGlrQWW/0WtT74FbFGh2qF/vMvAjOZwwNZP1FqmfwkRPjKGeaYlfvWAf9NXRe9weLMqA3La4bifwqzfeB6zWGD8JWq9/h1DtieCxN7qmYI97cajL+x0NJwkgkgAqLfzZxR8EANAGa4SsEB6AG1xTQAbHQ4NoDX+lIBJA2gheiYqVmX/aBMeHK2Rmdp75uMQkwzF38Ka1zt+znfvQU2xqnfX0tvNSGsEvXsp2NuM3UvxGqg7pP5sh800di9CCP9M+hUMnaFKwf5zvZSCCY+6Enjnp9BcoybH/egvR1c4HHeRKinfvxrKdhizT6G/7z9U368S+5j7s1CQSACot8scAVhUcKe+rOvnukfA6zcBTKOVgPk7BczhcLLdnQEURgGgTH8CQK4SMMkAEiIwBfNUPffse+C+0sit707pD6fep9Zf/R5qD7d/3GGHZU+p9UnXq8CYWfUfr6a/NdfAwfXG7mXLf9XtyDPM/WcmhDiadynY3uWw+kWjdyTMoKUB/nExVO9VF2Uvfrvjn+/J2WpaKMCGf4R2jyI4nE5Y9rRaT7kGouON3Y8BJADU28UkqLpW6L4MLJgZQFoPoPpWv163r7KBhhY7MVFWhmYm6r6voNEygOoOdT+1p8qVAZQmGUBC+M07+KM1fI7kJn8nXgkDjofmavj8iPTlLf+F8p1qtO2k/zNkez6z2tRVe4Bdi4zbh9MJWz9Wa5n+JUR4Sh8Kp/9erb96UDX1Fb2XwwEfXqOyO+PT4dL3u764PcFVBrbh3YjuC9Nr7PwKDm9S/SFPvNLo3RhCAkDCqw9QN9NWTNgDSCv/GtE3iShbGP11jk9XvwAquxlPKiVgQgTO8f/t3Xd4VHXa//HPpCekQGihq/SOIEtzpewqyIooFlRE2N0H147uuuzPVXfVteKKCj4iu9gbuI+i2LBtaFJEJPQmvQRCTYEQksz5/fGdkiEVMjMnM3m/rivXOTPnzDl3knOdydz5fu+72Df54xauRf4iIqUrXjD1jtZ9aP7QkUwiY9EUs97nD1JsCIyYvGCQWdqZAMraaEZqRsZKbS61Lw4A1dPrd2ZqaVG+9PEd4XfvR9V996j5h0hEtDT6XalBm4r3b3e5+cdJzr6aUZcO1eMe/dNrvPezWC0TQp+YETBVbQUfjBpAZzkFbPOBECwA7ebuBFbZf6KYAgacu8EPlN/qPVyL/DXtITXtadY//5NUmC9t+046sMZMgyg+LaVXoV283dwJoD0/mO/BDu7uX62HSLGJ9sQAoPrcU8FiEqU9y6Tlr9gdEeyw8k3vVOgrp0nnDaj8NdFxUtdrzXoG08BC2t6VpvV7RJTU93a7o7ENCSCUaAVv5wgg0978bEcAbToQgi3g3apSCPr0CdOuWWIEEICqcydPju2UFj7rHf3TuLO0+PnQaGVev42U1NS04N29zJ4YNrkSQEz/AkJf3ZbSZf8w6989Jh3+2d54EFzb50uf/9GsXzLJO7WrKrq7CgVv/FQ6le330BAk3z9vll2vr9X/WCcBhBJTwCobART4ItDHTp6WZVlVfp1nBFAodQBzq+8aAVRRAsg9+ic2RYoLwVFOAOzxq4elLteY9UVTpF3fm2lhe38oe0pcTeRw2NsO/thO6cBa0wGm3eXBPz8A/+v1W5MgLzolfcJUsFoja5M0+xbJWSR1uVYa/Neze32znlKD9mYK4fqPAxIiAuzwz9JGV02/AffYG4vNSADBOwUsd790Kqf8/QI5Asg1Bayw2FJeQdVa/uafLtaOIyckSe3DdQQQBaABnKtrXnV1enQl1S1n6CR/3Nzt4LfbkABy/6HYqr9Ux//vewBs4HBIV74kxSRJe5ZLy162OyIEWt4h6b3rTHOEFn2lkf9but17ZRwOb7twuoGFpiVTJVlSu2FSo452R2MrEkCQ4utKiWlmvbw6QJYlnXS1iU9o4PcQ4qIjlRBjpiRUtRPYloO5siypfp0YNUwMwda8VUkAeQpA195higDOkcMhjf3I+zgiOrSSP5J3BFBmhpR/LLjn9nT/GhHc8wIIrLotpKGPm/X/Pi4d3mpvPAicwnxp1o3S8d2mG9wN75qaPuei22gzknb3UjrJhZrcA97E3YB7bQ2lJiABBMMzDaycVvCn80zhUCkgI4Ak7yigo1UsBF1y+pfjbDP5NYE7AZSbaWr9lIUC0ACqY/Uss4yIlpyFodf2PrmpGcVkOaWdi4N33rwsb92hDr8J3nkBBEfPcaa4e9Ep6ePbmQoWjpxO87vdu0KKSzHt3utU45/YyU3MNSMxCijULH/FfI5t0Udq1c/uaGxHAghGZa3g3dO/ouKlmISAhOCpA1TFQtAbXQWg2zcO0do4CammraQkHS2nFTwt4AGcqwWTpfQnzLSvvx02y/QnQi8JdIEN08A2fS7JMt3USMAD4cfhMF2gYpNNgmDpS3ZHBH9Lf1xaP8d0fBr9jtSwXfWP6ZkGNsskmFDzncqRVrxm1gdMtDeWGoIEEAx3Aqi8KWABrP/jVs+VAKpqJ7CQLgDtVlkhaEYAATgXJZM/7mlfAyeFZhLI3dEsmIWgPdO/6P4FhK2U5tLQJ8z6f58ofxQ8Qs+qd6RFz5n1EVOl8y/xz3Hb/8Y0ZsneY9qJo+Zb+bqp/9SgPQ0dXEgAwWhQ2QigwHUAc0tNMK3gj1VhCphlWdrkTgCFYgFoN08doHLmEnuKQLcMTjwAwoOzuOyCz+4kUChNdzjvYlN34fAWKWd/4M93Kts72qjjlYE/HwD7XDhWavNrqbjATBcqrlojkqBIf6r8ZP2CyWY7StuxUPrUNdLjl3+SLhzjv2NHx0ldRpn1jPf8d1wERlGBtNRV6H3APVIEqQ+JBBDc3COAju0yBdPOFMQRQEeqMALoUF6Bjp44rQiH1LZROCSAyhgB5CyWcvaZdUYAATgbgx8ov+DzwElme6iIryc16W7WdywM/Pm2fG3qJTVoLzVoG/jzAbCPw2FGiMSmSPtWSkun2R2RV0Rk2SM23SM8IyLtiasmO7RFmn2zaffeeZQ0+CH/n6OHK6G0ca5UkOv/48N/1nwg5R2QkppIXa+3O5oagwQQjDoNzR/ZssruhhCEBFBqQtVrAG3KNDfc8+rXUXxMCL8BprqngJVRAyj3gGQVm7nLiY2DGxcA1CTBbAe/6VOzZPoXUDukNJOGPWnW05+UssoZDR9sJaftzn/adOQta3ovjBOHpXevNaM4m/eWrno5MCM+ml8k1W8jFZ6UNnzi/+PDP5xO6fsXzXrfO6SoGHvjqUFIAMFwOLzTwMqqA1TDagCFRf0fyTsCqKx2ku4C0MnN+C8PgNrNUwh6vvkQFCiF+dLWb8w67d+B2qPHGKntZaZTUE2ZClZcaEY/NuoszX9KerQeyZ/yFJ6SZt0kHd8l1W0l3fC+FB0fmHM5HN5i0EwDq7k2fyEd2WpG9/Uab3c0NQoJIHhV1Ao+GCOA3F3AqlADKOQ7gLl5WsHvl06f9N3mKQBNBzAAtVyLvlJkjLlXHvk5cOfZlm7+q5vSQmrSI3DnAVCzOBzSiBfNh8X9P0lLXrQnDqdT2rVU+uyP0j/bSe9dL2Wtd220zKhwkj++nE7pkzukPcvN72/Mf6TEhoE9Z7cbJDmkXd+X38kX9rEs6fsXzHrv30txIf550c9IAMGrolbwngRQ4IpA10uo+ggg9xSwkB8BlJAqxaWY9WM7fbcd322WdUkAAajlYhKkFn3M+vb5gTuPu/tXhyvMB0IAtUdyU+nyZ8z6/KelgxuCd+4D66Rv/i692E16fZj046tS/lGpTiMzncnNWRRaXRyDYf6T0roPXe3e3/J+ngmklGbeDpWrZwX+fDg7u5dKe1dIkbFSn9vsjqbGIQEEr4pawXu6gAVjBFBhhfsVFTv1c1aeJKljWohndB2O8juB0QIeALzc08AC1Q6+uMgMGZeo/wPUVt1vkNoNKzEVrOK/Savl2C7TqvzlftIrA8yIhew9UkySmZI29mPpot+bD7IXjjWviYwpuzB0bZXxnrTwWbN+xfPepEwwuItBr37PjEJCzbH4BbPscaOURB3VM5EAgpe7BtCRn0u/4QVxCtjxk6dV7Cy/xsOOwyd0utiphJhINa8XoPm9weQpBH1GJzASQADgdf4gs9yxKDBt7Hd9L+UfM+9zLfv5//gAaj6HQ7riBTM6OzPDO43EX/IOST/8W3r1MjPa57vHpKwNJrHTcYR0/VvSn7eaAsZ7V0gLnjI1f0ZMleq2NImpTleRBJLMe8Hce8z6xfdJPW8J7vk7/EaKTTYj9ncvCe65Ub6DG6StX0lySP3vsTuaGokEELxSmksxiWZ46ZnzWYOQAKqbEC1JclpSTn75/3HZ5CoA3T4tSRERYTBEv7xC0O4i0NQAAgCp6YXmj+1Tx6UDa/x/fPf0r/bDKbwP1GbJTaTLXaNK5j8jHVxf8f6VKciVVs+W3rlGeq699MX9pl6NHKbD4cj/le7fKo1+R+o00lu82FnsLfgcEeGqOyPpdJ55PhCJ8FBxeKur3XuhSYgN+VvwY4hJkDpfZdYpBl1zLJlqlh1HSPVb2xtLDUUCCF4Oh9SgrVkvWQfI6QzKFLDoyAglx0VJko5WUAh6k6sAdIe0EK//4+aZAlbeCCASQACgyCip1QCz7u86QE6ntNGVAKL7F4Bu15tksLPw3KaCFZ2WNn0h/ee30rNtpTm3Sj9/K1nFUtOe0tCnpD9tksbNlS68WYqvW/oYgx/wLfjc3ZUA2vZfqec4s702OnFEevc688+AZhdJV78SmHbvVeGeBrb+Y6kgz54Y4HV8j7T2P2b94nttDaUmIwEEX55W8CU6gRVkmzcsKaBFoKUSdYAqKATtaQEf6vV/3NzZ6ZKjrk5lSwUm0cUUMABw8bSD93MdoP2rTIexmETzH3kAtZvDYWrKxNWVMldLi5+v/DVOp3da0j/bSrNulNZ/JBXlS/XbSIMekO7+Sbo1Xep3h5SUdnYx1W9tCkJbTu+H3NqmqECaPUY6tsNMibsxgO3eq6JFH/OP3MIT0sZP7YsDxrKXzUyW834pNetldzQ1VpTdAaCGKasVvHv0T0ySFBUb0NPXqxOjnUdOVtgJbGOmdwpYWHCPAMrZKxXmmzey467pXwn1zRBTAIC3wOfuZeaDgL/ekza5/nBve5kUHeefYwIIbUlp5oPkpk9NvZ32w6W0Lt7tCyabD5sdfmMSMms/NIlkt8Q0qeu15qtJD/90Fux+g6kNtHqW1P+u6h8vlFiW9MmdpsNTbLJ003+kxEb2xuRwSN1vktIflzLeNUWHYY+TR6WVb5p1Rv9UiBFA8FVmAijwLeDdUhPcncDKTgDlnCrUvuP5ksJoClhCffNGJnlbwVMAGgBKa9hBSmxs/qO+5wf/HNOyvP+5pfsXgJLSupqls1D6+DbvVLAv/58pxLxipjTjEmnJNJP8iU0xHbvGfSr9cYM09AlTv8wfyR9J6jxKioiWDq41reNrk/lPm0SbI1K6/k2pUQe7IzK63yDJIe1cZDq7wR4rXjUjsRp3lVr/yu5oajQSQPDV0HUzPbzV29IwCAWg3eq5poAdPVH2XOstrulfaclxqutKFoW8kq3g3YWgKQANAKU5HNL5l5h1f7WDP7TZdL+MjJHaXOqfYwIID4P+IvWfaNYPrJX+77fScx2l5dPNcyePSFFxphDx6HdNB6+RL5n7VCCKySekSu2GmvU1s/x//Jpq9WxpwdNm/YopUush9sZTUt0W3velNbPtjaW2KsyXlr9i1gdM9F/CNUyRAIKvuq3MH8FF+VL2bvPcicNmWadBwE/vqQFUzgigje76P03CZPSP25mFoEkAAUDZ3DV6/FUI2j3654LBUlyY1JYD4D+XPWYSPJK5X+Tul+QwSYirppsOXte/aUYQBrhUgiSpu2ua0Zr/hGcnsPSnfFvc71oizXVNd2vRV8rJtCeuivS4ySwz3jWjShFcGe9KJw9LKS2lzlfbHU2NRwIIviKjpPruTmBbzDKYI4AS3COAyk4AbfZ0AAuzP9I9haDdCSDXFLC6JIAAwIe7EPS+n6RTOdU/3iamfwGoxHVvSA7XxyZHpHT/FmnsHPPBP9iJ47aXSfH1pLwD/u+IWBNERJrpdQsmm5Hxs26Sik+bRjV7lgVmZFV1dRxhmggc22lqFCF4iovMFEzJ1MWKpMRxZUgAobSG7czS3Qo+iAmg1DrRksrvArYp090BLFxHALmmgLmLQFMDCAB81W0p1TvfdKfc9X31jnV8t+nw44gwBV4BoCwLnzXdtyJjzL1n5Rv2xRIVI3W5xqyvDsNpYAMnSYMfNEmgmb+S8o9JSU1Mh+LBD5rtNU1MHanzVWY94z1bQ6l1Nn5iEm/xqdKFN9sdTUggAYTSzmwF7+4CFoQi0J4RQGVMAbMsy9sCPmyngLlawVMEGgDK5+4GVt128Bs/M8uW/YMyzRlACFow2SQjBj8oPXzIm5woOU0p2NzTwDZ9JhXk2hdHoAycZFqs5x8zj3Mza27yx627axrY+o+l0ydsDaXWsCxp8Qtmvc8fTCIOlSIBhNLO7AQW1BFArhpAZYwA2nc8X7kFRYqKcOiCBokBjyWoUl1TwLL3mjfyXNf85pSW9sUEADWVexpYdQtBb3IlgJj+BaAsJZM/7uRDyREqdiWBmvWS6reRCk9665iFk4I8KWuT93FkTM1O/khSy35SvfOk07nefy4gsLbPlw6skaLipd4T7I4mZJAAQmmeBNAWk1m1pQtY6QSQe/RPm0aJiokKs0u3TgMpJkmSZYrdyTJdJfiPNACUdp6r40rWBin34LkdI++Qt1ZDBxJAAMrgLC575Ik7CWRXEWaHQ+p2g1lf/b49MQTS6velgmyzHhljagDZOeKqKiIivKOAVjMNLCi+f8Ese94i1Qn859RwEWafouEX9duYeggF2VLugeCOAHJNAcs5VaTCYqfPtk2uBFD7cKv/I7lawZ9v1ncsNMuU5rQxBICy1KkvpXU16+575tna/IWp6dGkBwX3AZRt8APljzwZOMlst0u3681yxyJv6YBw4HRK6U+a9baX1Zxpd1XR3ZWU277AW88TgbF/lRkB5IiU+t1pdzQhhQQQSouKNQU2JVMHKIgJoOT4aEW4ch5ntoJ3J4DCrgOYm7sTmLumBfV/AKB87nbwO+af2+uZ/gUglNVrJbW6WJIlrfnA7mj85+PbpPyjUmSsdO1r5rmaMO2uKuq1ks77pczvJAwLdNck3081yy7XmJ87qowEEMrWsINZHlwvnTpu1oOQAIqMcKhugrsOUKHPtk2Z7hbwYTgCSPIWgj641ixJAAFA+TyFoBea6cpn41SOt31yxyv9GRUABE/30Wa5etbZ3wdrqj3LzfIXE6TYEn/z2z3trqrcBboz3g+f30lNc3S7tOFjsz7gHltDCUUkgFA2dyv43ctcTzikuLpBOXW9BNMKvmQdoIKiYm0/bCrqh10HMDd3IWg3CkADQPla9pMioqTs3dKxHWf32q1fm5oS9dt6694BQKjpNNLUjDy8WcrMsDua6svaZFp6OyJMAuhMdk+7q4pOI6XoOtLRbdKeH+yOJjwteclM4W7za+90cFQZCSCUzT0CyF0gM76uFBkVlFPXrxMryXcK2M9ZeSp2WkqJj1ZaclxQ4gg69wggN0YAAUD5YhOl5r8w62fbDp7pXwDCQVyK1H64WV8dBlOOlr9ilu2Hm45aoSg20SSBJCnjXXtjCUd5h7w/1wH32hpKqCIBhLI1cI0AOnHILIMw/cutXp3SI4A2ZXoLQDvCtTDymQkgipICQMXOpR184Slp6zdmveMI/8cEAMHknnK09v+k4sKK963JTh71JrH63m5vLNXVw/U7WT9HKsy3N5Zw88MMqeiU1LSndN7FdkcTkkgAoWzuBJBbEBNAqXXcNYC8CaDNB00CqGO41v9Jf0pa+YYUk+h9zj0CaMFksx0A4MtdCHr7AtM5piq2z5dO50nJzcwfkAAQyloPkeo0lE4eln7+zu5ozt1Pb0pF+WZKT6sBdkdTPa0uNqUcCnKkTZ/bHU34KMiTfvi3Wb/4XrolnyMSQChbbKKUUmIESjBHALmKQB8tMQVso6sAdPtw7QAWESnNf1KKSfA+l9zMlfx5wmwHAPhq1svUWsg/Kh1cV7XXbPrULDv8hj8eAYS+yCip63VmffX79sZyrooLvR/s+94R+vfmiAjvKCCmgfnPT2+a5kSpraUOTOE+VySAUL6So4ASUoN22jJHALlbwIdrAWh3Z4O8LPM4MU36/kWT/Bn8oNkOAPAVFSO16m/WqzINrLhI2vylWWf6F4Bw0f0Gs9z8pZR/3NZQzsnGuVLOPjOSqcs1dkfjH+7fyfb5Us5+W0MJC8WF0tL/Nev97+af49VAAgjlcxeClmwaAWTmMR89cVpZuQWSpHaNwzQBJJkkT0vXB5m8gyR/AKAqLigxDawyu5dKJ49I8ane+y0AhLq0blKjTlJxgbc9dihZ5ir+fNHvpahYe2Pxl9QLzPuM5QyPAt12W/t/riRhI2/dK5wTEkAoLf0pM/WoYckRQK4EUBDq0Zw5AmjTATP9q2VqghJjg9OJzDb97nStWFJkDMkfAKjMBYPMctcSqeh0hbt6un+1Hx60zpYAEHAOh9RttFkPtWTD3pXS3h+kiGjpot/ZHY1/9bjJLDPekyzL3lhCmdNpZkZIpkB4dJh2hA4SEkAoLSLSjD7Zt9L7XEL9oNWjqedKALm7gJXsABb2Dq43y8gYqfi0+ZkDAMrXqLN5jyo84fu+dSbLkjbS/h1AmOp2vSSHGel4dIfd0VTd8ulm2fVaKamxvbH4W6eRUlS8dGRrxe9PqNjP30iHNkoxSeGXJLQBCSCU5q5H89Nb3ue2LwjalKRU1xSwYyd9RwCFbQcwtwWTTSHowQ9KDx8yy/QnSAIBQEUiIqTzLzHr2+eXv9/+VVLOXlM0+oLBQQkNAIImual3ROSaD2wNpcpy9ptW6ZLU5zZ7YwmEuGSp05VmnWLQ527xC2Z50Xgpvq6NgYQHEkAomzsJ5Lb2g6DVo6lXJ1qSdPJ0sU4VFpcoAB2mHcAk7+iqkj9j9++AJBAAVMzdDr6iQtDu6V9tL2X4OIDw5C48vPr90JhytGKm5CwytXKa9rA7msBwTwNb96FUeMreWELRnh+k3UvMFMG+d9gdTVggAYTyDZwkRbhqJASxHk1ibJSiI037x8N5Bdp8sBZMAXMWl51gcyeBnMX2xAUAocBdCHrvCqkgr+x9Nrrav9P9C0C46nCFGeV4bIe5H9ZkhfnSj6+b9b632xtLIJ13iZTcXDqVLW3+wu5oQo979E/30WaUG6qNBBDKt2CyycoHuR6Nw+HwdAJbvSdbpwqdio2K0Hn16wTl/LYY/ED5CbaBk8x2AEDZ6p0vpbQ071m7l5befmiLdHiL+Q9i20uDHx8ABENsonfK0er37Y2lMms+kPKPmnt3h9/YHU3gRER4R2ZlvGdvLKHm0BZp8+dmvf899sYSRkgAoWwlpyTZUI/G3QlsybbDkkz798gIR1DODQAIMQ5HiXbw80tv3+Qa/XPBICkuJVhRAUDwubuBrftIKiqwN5byWJa03NX6vc+tAW8wYzv3NLBt30k5mfbGEkqWuDp/tf+N1LC9vbGEERJAKK0G1KNxjwBauv2IJKlDOE//AgBUn7v4aVl1gDzTv+j+BSDMnX+JlNRUOnVc2vKV3dGUbccCKWuDma524Vi7owm8+q2lFn0ky2nqqqJyOful1bPN+sX32hpKuCEBhNJqQD0a9wig7YdOSArz+j8AgOpzdwI7sFY6cdj7fPZe0wFMDqn9cFtCA4CgiYh0tYSXtHqWvbGUZ5mr9XuPm2pPVyf3KKCM90KjQLfdlk2XnIVSy35Si1/YHU1YIQGE0mpAPRp3JzC3juHcAQwAUH2JjaRGncz6joXe5ze56ge07Gf2AYBw5645s/Ur6cQRe2M505Ft3pFJ4dj6vTydr5ai4qRDm6T9P9kdTc2Wf9xbIHzAvXZGEpZIAKFGSnVNAXNjBBAAoFJltYNn+heA2qZRR6lJd1MYf/1Hdkfja/kMSZbU9jKpQRu7owmeuBTTpU2SMmp4gW67/fiadDpXatjRXCfwKxJAqJFSErwjgJLjoj01gQAAKJenELQrAXTiiLTre7PegQQQgFqk+41mWZO6gZ3KljLeNevh3Pq9PO5pYGv/U3MLdNut8JR3iuCAiaaLGvzK1p/o9OnT1a1bNyUnJys5OVn9+vXTl19+6dluWZYeeeQRNW3aVPHx8Ro0aJDWr19vY8QIhnnrMjXtu589j3NOFeriZ/6reeuomg8AqECrAZIjUjq2Qzq+W9rypSm6mdZNqtfK7ugAIHi6XGvuh/tWSoe32h2Nseod6XSe1LCDdMFgu6MJvgsGeQt0b/6ysr1rpzWzpBNZUnIzqcs1dkcTlmxNADVv3lxPP/20fvzxR/34448aMmSIRo4c6UnyTJ48WVOmTNFLL72kFStWKC0tTZdeeqlyc3PtDBsBNG9dpm5/5ycdzy/0ef5A9ind/s5PJIEAAOWLS5aa9TLr2xeUmP41wr6YAMAOiQ2lNr826zWhGLSz2DX9S6b2j8Nhbzx2iIiUuo826zVpZFZN4SyWvp9q1vvdKUUxAyQQbE0AjRgxQsOHD1e7du3Url07PfHEE0pMTNSyZctkWZZeeOEFPfjggxo1apS6dOmiN998UydPntR7771nZ9gIkGKnpUc/3aCy6uK7n3v00w0qdlI5HwBQDvc0sE2fS9vSzToJIAC1kTvZsGa25HTaG8vmL6Xju6T4elK30fbGYqfurmlgW7+Rcg/aG0tNs+kz6eg2Ka6u1HOc3dGErRozqa64uFizZs3SiRMn1K9fP+3YsUMHDhzQZZd5Cz/FxsZq4MCBWrJkSbnHKSgoUE5Ojs8XQsMPO44qM/tUudstSZnZp/TDjqPBCwoAEDrSn5Jy9pv1LV9KxQVSamsz3WDBZLMdAGqL9sOl2GQpe4+0u/zPT0HhruvSa7wUk2BrKLZq2E5q3luyiqW1H9gdjb3SnzLvzZJkWdLiF8x67/+Rlr3Me3aA2J4AWrt2rRITExUbG6vbbrtNc+bMUadOnXTgwAFJUuPGjX32b9y4sWdbWZ566imlpKR4vlq0aBHQ+OE/WbnlJ3/OZT8AQC0TEWkKjEZEeZ/reIW08Fkp/QmzHQBqi+h4qfNVZt3OKUeZa6Rdi01Not4T7IujpnAX6M54zyQ+aquISPPevGCytHOxtP8nKSrOdK/jPTtgbE8AtW/fXhkZGVq2bJluv/12jRs3Ths2bPBsd5wxP9SyrFLPlfTAAw8oOzvb87Vnz56AxQ7/apQU59f9AAC1zMBJ0uAHzR+PbqeyzR+Sgx802wGgNul2g1mu/0Q6fdKeGJa/Ypadr5JSmtkTQ03SZZQUGStlbZAyV9sdjX3c79npT0hz7zbPNeokff8C79kBZHsCKCYmRm3atNFFF12kp556St27d9eLL76otLQ0SSo12icrK6vUqKCSYmNjPV3F3F8IDb84P1VNUuJUXnrPIalJSpx+cX5qMMMCAISSgZN8u8usfIM/JAHUXi37SXVbSqdzpc1fBP/8eYdM23NJ6lMLW7+XJb6e1OE3Zj2jFte2PbpDKj4txSaZ7p2SGQXEe3ZA2Z4AOpNlWSooKND555+vtLQ0ffPNN55tp0+f1oIFC9S/f38bI0SgREY49PcRnSSpVBLI/fjvIzopMqIWdg0AAFTdqH/L884RGcMfkgBqr4gI7yggO7qB/fia+ZDf7CKpRe/gn7+m6uEqBr32P1LRaXtjCabTJ6XVs6U3rpCm9jBTtAtKdPjmPTvgbE0A/fWvf9WiRYu0c+dOrV27Vg8++KDmz5+vMWPGyOFw6N5779WTTz6pOXPmaN26dRo/frwSEhJ000032Rk2AmhYlyaafnNPpaX4TvNKS4nT9Jt7aliXJjZFBgAIGStfl2SZPySLT3uLTAJAbdTdlQDa9l1wO08VFUgrZpr1voz+8XHBYCkxTco/Km39yu5oAsuypL0rpU/vlZ5rL825Vdq5SJJDav0rqdPVZj/es4MiqvJdAufgwYMaO3asMjMzlZKSom7dumnevHm69NJLJUmTJk1Sfn6+7rjjDh07dkx9+vTR119/raSkJDvDRoAN69JEl3ZK0w87jior95QaJZlpX4z8AQBUasFk35o/7scS/1UEUDvVb206T+1dIa37P6nfncE57/o50oksKamJ1GlkcM4ZKiKjpO6jpe9fNNPAOo6wOyL/O3FYWjNb+ult6dBG7/N1W0kX3myKYa9+n/fsIHNYVniXHs/JyVFKSoqys7OpBwQAQDg7M/lT2fMAUFusmCl9/icprat02+LAn8+ypH8NNEWOf/U36Zd/Cvw5Q03WJunlPqZz5R83SYkN7Y6o+oqLzEizVW9Lm7/0NmWIipM6Xin1HCu1uthMTeQ922/OJudh6wggAAAAv3EWl/0Ho/uxszj4MQFATdB5lPTl/5MOrJUOrpcadw7s+XYvM8mfqDip128De65Q1aiD1LSnKXy89j9SvzvsjujcHdkmrXrHjOjJzfQ+37SnGe3T5Ropvq7va3jPtgUJIAAAEB4GP1D+Nv6LCKA2S0iV2g2VNn1mikFf9o/Anm/Zy2bZbbQ5N8rW4yaTAMp4L/QSQAV50oZPTOJn9xLv8/Gppu7UhTdXnGjkPdsWJIAAAAAAINx1v9EkgNb+R/r1I1JEZGDOc2yXOY8k9bktMOcIF12ukb76q3RwrZS5RmrSze6IKmZZppbUqreldR9Jp/PM844IU9C551ip3eVSVIy9caJcJIAAAAAAINy1vUyKr2em6OxYILUeEpjzrPi3ZDmlCwZJjTsF5hzhIiFVan+5GUmz+v2amwDKPSitmWVG+xze4n2+3vnegs4pzeyLD1VGAggAAAAAwl1UjBlxsmKmmQYWiARQQZ608i2z3jfEpjTZpccYkwBa84F06WNSZHTwzp3+lBkJVtaUq/SnpMNbpaJT0pZ5kuWqyROdIHW6yiR+WvWXHHRqDiUkgAAAAACgNuh+o0kAbfzUJGtiE/17/NXvSwXZUmprqc2l/j12uGr9K6lOI+lElrT1G6nD8OCdOyKydNv1Q1ukuXdLe5b57tu8t0n6dB4lxdFdO1SRAAIAAACA2qBZL5OcObrNJIF63Oi/Yzud0vJXzHqf20yrb1QuMkrqdr209CUp493gJYCKCqSu10rHdpok0M7FZrTPnuXefRIauAo6jzVdyxDySAABAAAAQG3gcJhRQOmPm9E6/kwA/fytdORnKTbZv8cNd+lPmWSMJG35SjpxRKpT3zxeMNnVLr2CjlllKTot5e6XsvdJOfulnL2l108e9n3NjgXe9fptTaHwdkODOyUNAUcCCAAAAABqi27XmwTQjoUmEeCv4r3Lp5tlz1uk2CT/HLM2iIg0hbMT06S8A9K6/5P6/MEkf9KfkAY/6Lt/caEp5J29T8pxfZ25fiKraueOipOSm0nJTaVd35vi3ZHR0t0/+v/7RI1AAggAAAAAaot6raRWA8wH/rUfSBffV/1jZm2Stv3XtAP/xYTqH682cdfecdfiWfW2dHCd9NNbpj7QyaPS7JvN6J3sfVLeQUlW5ceNjDWJnZTmZpnczCT7kkt8JaSaUWELJks7F0mRMVLxafO4rMLQCHkkgAAAAACgNul+g0kArZ4lDbi3+p2c3LV/2g+X6p1X3ehqn4GTpNMnpe+flw6sNV+StO0783WmyBhvUsc9gielue96Qv2q/V5LjjQaOMn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      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming 'result_df' is your DataFrame with the percentage data\n",
    "\n",
    "# Plotting the trends\n",
    "plt.figure(figsize=(14, 7))  # Set the figure size\n",
    "plt.plot(result_df['publication_decade'], result_df['Frequent_Authors_Percent'], label='Frequent Authors', marker='o')\n",
    "plt.plot(result_df['publication_decade'], result_df['Non_Frequent_Authors_Percent'], label='Non-Frequent Authors', marker='x')\n",
    "\n",
    "# Adding title and labels\n",
    "plt.title('Percentage of ESTC IDs by Author Type per Decade')\n",
    "plt.xlabel('Publication Decade')\n",
    "plt.ylabel('Percentage (%)')\n",
    "\n",
    "# Adding legend\n",
    "plt.legend()\n",
    "\n",
    "# Displaying the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "922d78fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts=ww3.pivot_table(index='authors_variable', \n",
    "                     columns='publication_year', \n",
    "                     values='estc_id',\n",
    "                     fill_value=0, \n",
    "                     aggfunc='count').unstack().to_frame().rename(columns={0:'estc_id'}).reset_index()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd0e006e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4474\n",
      "2579\n"
     ]
    }
   ],
   "source": [
    "print(counts[counts['authors_variable']=='Frequent_author']['estc_id'].sum())\n",
    "print(counts[counts['authors_variable']=='other']['estc_id'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95938230",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_publications=estc_core.groupby('publication_year').count()['estc_id'].to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c2ff37d",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_publications['publication_year']=total_publications.index\n",
    "total_publications.rename(columns={'estc_id':'count'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7deaae36",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_publications.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81ef1a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "#here I cut out 16th and 15th century\n",
    "seventeeth_frequent=counts[(counts['publication_year']>1599) & (counts['publication_year']<1700) & (counts['authors_variable']=='Frequent_author')]\n",
    "seventeeth_frequent=seventeeth_frequent.merge(total_publications, on='publication_year')\n",
    "seventeeth_other=counts[(counts['publication_year']>1599) & (counts['publication_year']<1700) & (counts['authors_variable']=='other')]\n",
    "seventeeth_other=seventeeth_other.merge(total_publications, on='publication_year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "131c65d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "seventeeth_frequent['non_classics']=seventeeth_frequent['count']-seventeeth_frequent['estc_id']-seventeeth_other['estc_id']\n",
    "seventeeth_other['non_classics']=seventeeth_other['count']-seventeeth_other['estc_id']-seventeeth_frequent['estc_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ee17d91",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here I take the full dataset\n",
    "#seventeeth_frequent=counts[(counts['publication_decade']<1700) & (counts['authors_variable']=='Frequent_author')]\n",
    "#seventeeth_other=counts[(counts['publication_decade']<1700) & (counts['authors_variable']=='other')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bea7479",
   "metadata": {},
   "outputs": [],
   "source": [
    "eighteenth_frequent=counts[(counts['publication_year']>=1700) &(counts['publication_year']<1800)& (counts['authors_variable']=='Frequent_author')]\n",
    "eighteenth_frequent=eighteenth_frequent.merge(total_publications, on='publication_year')\n",
    "eighteenth_other=counts[(counts['publication_year']>=1700) &(counts['publication_year']<1800)& (counts['authors_variable']=='other')]\n",
    "eighteenth_other=eighteenth_other.merge(total_publications, on='publication_year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47a4d587",
   "metadata": {},
   "outputs": [],
   "source": [
    "eighteenth_frequent['non_classics']=eighteenth_frequent['count']-eighteenth_frequent['estc_id']-eighteenth_other['estc_id']\n",
    "eighteenth_other['non_classics']=eighteenth_other['count']-eighteenth_other['estc_id']-eighteenth_frequent['estc_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0381e0f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>publication_year</th>\n",
       "      <th>authors_variable</th>\n",
       "      <th>estc_id</th>\n",
       "      <th>count</th>\n",
       "      <th>non_classics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1700.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>36</td>\n",
       "      <td>2851</td>\n",
       "      <td>2793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1701.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>23</td>\n",
       "      <td>2472</td>\n",
       "      <td>2436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1702.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>20</td>\n",
       "      <td>2241</td>\n",
       "      <td>2200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1703.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>18</td>\n",
       "      <td>1934</td>\n",
       "      <td>1903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1704.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>24</td>\n",
       "      <td>2115</td>\n",
       "      <td>2079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1795.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>47</td>\n",
       "      <td>9424</td>\n",
       "      <td>9354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1796.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>21</td>\n",
       "      <td>7824</td>\n",
       "      <td>7783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1797.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>26</td>\n",
       "      <td>7128</td>\n",
       "      <td>7080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1798.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>22</td>\n",
       "      <td>8009</td>\n",
       "      <td>7973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1799.0</td>\n",
       "      <td>Frequent_author</td>\n",
       "      <td>28</td>\n",
       "      <td>7346</td>\n",
       "      <td>7306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    publication_year authors_variable  estc_id  count  non_classics\n",
       "0             1700.0  Frequent_author       36   2851          2793\n",
       "1             1701.0  Frequent_author       23   2472          2436\n",
       "2             1702.0  Frequent_author       20   2241          2200\n",
       "3             1703.0  Frequent_author       18   1934          1903\n",
       "4             1704.0  Frequent_author       24   2115          2079\n",
       "..               ...              ...      ...    ...           ...\n",
       "95            1795.0  Frequent_author       47   9424          9354\n",
       "96            1796.0  Frequent_author       21   7824          7783\n",
       "97            1797.0  Frequent_author       26   7128          7080\n",
       "98            1798.0  Frequent_author       22   8009          7973\n",
       "99            1799.0  Frequent_author       28   7346          7306\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eighteenth_frequent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb68ac01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:                estc_id   No. Observations:                  100\n",
      "Model:                            GLM   Df Residuals:                       97\n",
      "Model Family:                 Poisson   Df Model:                            2\n",
      "Link Function:                    Log   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -324.34\n",
      "Date:                Fri, 08 Nov 2024   Deviance:                       237.33\n",
      "Time:                        12:59:19   Pearson chi2:                     223.\n",
      "No. Iterations:                     4   Pseudo R-squ. (CS):             0.7684\n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept          -22.7625      2.374     -9.588      0.000     -27.416     -18.109\n",
      "publication_year     0.0154      0.001     10.532      0.000       0.013       0.018\n",
      "non_classics        -0.0002   6.29e-05     -3.545      0.000      -0.000   -9.96e-05\n",
      "====================================================================================\n",
      "[ 6.22462517  6.52165288  6.5190777   6.4673275   6.57956581  6.7446497\n",
      "  6.81295493  6.87582159  7.01387595  7.03457611  7.24005299  7.35750109\n",
      "  7.39237949  7.41089378  7.6256636   7.58538168  7.81216713  8.01888968\n",
      "  7.9676499   8.16210414  8.10272506  8.27462603  8.31570456  8.53765346\n",
      "  8.5991749   8.56710097  9.20680202  9.28968803  9.28808895  9.49994184\n",
      "  9.23126296  9.54971422  9.81115465  9.82477618 10.12753577  9.93362611\n",
      " 10.55710435 10.58354473 10.5346769  10.82067139 10.44459576  7.93970174\n",
      "  5.53695872  8.30047745 10.0226502  10.28328257 10.16761496  9.25569621\n",
      "  8.49152382  9.77594746 10.56930063 11.69489978 11.75033971 11.41547114\n",
      " 11.83561082 12.03833154 12.44529736 12.51265501 12.63937466 10.98229505\n",
      "  8.78543572 12.14978511 13.22092918 13.5949858  14.77707063 14.97001804\n",
      " 16.12011244 16.18210265 16.36055762 16.37232552 14.81123046 16.08831109\n",
      " 15.80490128 16.36834731 15.79946057 16.1922673  17.19323646 17.30941453\n",
      " 16.8949815  15.55197806 13.84830326 14.62921749 15.43008504 16.13759189\n",
      " 16.58677643 15.42211821 19.77204106 19.12316429 16.36619468 14.13207719\n",
      " 15.11305412 18.54378283 19.58511407 19.96055228 20.75521548 17.14472948\n",
      " 19.51502858 20.59712903 20.60735253 21.05861225]\n",
      "BB_LAMBDA    0.082615\n",
      "dtype: float64\n",
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:                estc_id   No. Observations:                  100\n",
      "Model:                            GLM   Df Residuals:                       97\n",
      "Model Family:        NegativeBinomial   Df Model:                            2\n",
      "Link Function:                    Log   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -305.61\n",
      "Date:                Fri, 08 Nov 2024   Deviance:                       134.11\n",
      "Time:                        12:59:19   Pearson chi2:                     118.\n",
      "No. Iterations:                     5   Pseudo R-squ. (CS):             0.5117\n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept          -22.4571      3.332     -6.739      0.000     -28.988     -15.926\n",
      "publication_year     0.0152      0.002      7.407      0.000       0.011       0.019\n",
      "non_classics        -0.0002    8.7e-05     -2.700      0.007      -0.000   -6.44e-05\n",
      "====================================================================================\n",
      "         mean   mean_se  mean_ci_lower  mean_ci_upper\n",
      "0   16.571585  1.755130      13.465161      20.394663\n",
      "1   18.298610  1.617718      15.387436      21.760555\n",
      "2   19.639211  1.617380      16.711816      23.079396\n",
      "3   21.382332  1.763599      18.190665      25.133997\n",
      "4   20.831289  1.732298      17.698299      24.518888\n",
      "..        ...       ...            ...            ...\n",
      "95  15.097280  8.226608       5.188877      43.926243\n",
      "96  22.173908  9.339701       9.712123      50.625613\n",
      "97  26.558638  9.799540      12.886399      54.736879\n",
      "98  21.862337  9.498005       9.330297      51.226856\n",
      "99  25.964881  9.981183      12.222927      55.156598\n",
      "\n",
      "[100 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "#Setup the regression expression in patsy notation. We are telling patsy that BB_COUNT is our dependent variable and it depends on the regression variables: DAY, DAY_OF_WEEK, MONTH, HIGH_T, LOW_T and PRECIP\n",
    "expr = \"\"\"estc_id ~ publication_year +non_classics \"\"\"\n",
    "\n",
    "#Set up the X and y matrices for the training and testing data sets\n",
    "y_train_freq, X_train_freq = dmatrices(expr, seventeeth_frequent, return_type='dataframe')\n",
    "y_test_freq, X_test_freq = dmatrices(expr, eighteenth_frequent, return_type='dataframe')\n",
    "\n",
    "#Using the statsmodels GLM class, train the Poisson regression model on the training data set\n",
    "poisson_training_results_freq = sm.GLM(y_train_freq, X_train_freq, family=sm.families.Poisson()).fit()\n",
    "\n",
    "#print out the training summary\n",
    "print(poisson_training_results_freq.summary())\n",
    "\n",
    "#print out the fitted rate vector\n",
    "print(poisson_training_results_freq.mu)\n",
    "\n",
    "#Add the λ vector as a new column called 'BB_LAMBDA' to the Data Frame of the training data set\n",
    "seventeeth_frequent['BB_LAMBDA'] = poisson_training_results_freq.mu\n",
    "\n",
    "#add a derived column called 'AUX_OLS_DEP' to the pandas Data Frame. This new column will store the values of the dependent variable of the OLS regression\n",
    "seventeeth_frequent['AUX_OLS_DEP'] = seventeeth_frequent.apply(lambda x: ((x['estc_id'] - x['BB_LAMBDA'])**2 - x['BB_LAMBDA']) / x['BB_LAMBDA'], axis=1)\n",
    "\n",
    "#use patsy to form the model specification for the OLSR\n",
    "ols_expr = \"\"\"AUX_OLS_DEP ~ BB_LAMBDA - 1\"\"\"\n",
    "\n",
    "#Configure and fit the OLSR model\n",
    "aux_olsr_results_freq = smf.ols(ols_expr, seventeeth_frequent).fit()\n",
    "\n",
    "#Print the regression params\n",
    "print(aux_olsr_results_freq.params)\n",
    "\n",
    "#train the NB2 model on the training data set\n",
    "nb2_training_results_freq = sm.GLM(y_train_freq, X_train_freq,family=sm.families.NegativeBinomial(alpha=aux_olsr_results_freq.params[0])).fit()\n",
    "\n",
    "#print the training summary\n",
    "print(nb2_training_results_freq.summary())\n",
    "\n",
    "#make some predictions using our trained NB2 model\n",
    "nb2_predictions_freq = nb2_training_results_freq.get_prediction(X_test_freq)\n",
    "\n",
    "#print out the predictions\n",
    "predictions_summary_frame_freq = nb2_predictions_freq.summary_frame()\n",
    "print(predictions_summary_frame_freq)\n",
    "\n",
    "predicted_counts_freq=predictions_summary_frame_freq['mean']\n",
    "actual_counts_freq = y_test_freq['estc_id']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3f8d18d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:                estc_id   No. Observations:                  100\n",
      "Model:                            GLM   Df Residuals:                       97\n",
      "Model Family:                 Poisson   Df Model:                            2\n",
      "Link Function:                    Log   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -331.90\n",
      "Date:                Fri, 08 Nov 2024   Deviance:                       286.45\n",
      "Time:                        12:59:31   Pearson chi2:                     323.\n",
      "No. Iterations:                     4   Pseudo R-squ. (CS):             0.8246\n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept          -28.0850      2.739    -10.255      0.000     -33.453     -22.717\n",
      "publication_year     0.0184      0.002     10.935      0.000       0.015       0.022\n",
      "non_classics        -0.0002   7.09e-05     -2.735      0.006      -0.000    -5.5e-05\n",
      "====================================================================================\n",
      "[ 3.83288473  4.01172945  4.03048205  4.02269622  4.10392821  4.21451283\n",
      "  4.27300425  4.3289463   4.4266576   4.46031266  4.59652631  4.68479863\n",
      "  4.72774935  4.76184529  4.90631537  4.90825278  5.0610913   5.20352212\n",
      "  5.20052905  5.33755481  5.33034597  5.4559511   5.50704028  5.66311986\n",
      "  5.72725099  5.7372999   6.13931573  6.21848476  6.24876506  6.40470518\n",
      "  6.27801684  6.49867899  6.68675466  6.72844778  6.94335242  6.86170105\n",
      "  7.27161398  7.32405594  7.33121434  7.54192056  7.34982925  5.81751315\n",
      "  4.27155326  6.10793579  7.23400798  7.43470397  7.3987765   6.85161184\n",
      "  6.38814801  7.25815374  7.80753272  8.56969417  8.64826202  8.47555027\n",
      "  8.79049217  8.96626233  9.27601081  9.36650491  9.49649864  8.4447027\n",
      "  6.987822    9.31419867 10.07573915 10.37538668 11.21280761 11.39713777\n",
      " 12.21705773 12.31949967 12.50017481 12.57079605 11.5780687  12.50527935\n",
      " 12.3750185  12.8224143  12.49582981 12.83005325 13.58596246 13.73448365\n",
      " 13.5151549  12.63767738 11.48041497 12.10267514 12.74134545 13.31517951\n",
      " 13.70584657 12.92838145 16.13231671 15.74890803 13.8210515  12.22369314\n",
      " 13.02449708 15.642738   16.48740451 16.84647158 17.51659641 14.90533593\n",
      " 16.76854187 17.66371375 17.76006063 18.18914642]\n",
      "BB_LAMBDA    0.201457\n",
      "dtype: float64\n",
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:                estc_id   No. Observations:                  100\n",
      "Model:                            GLM   Df Residuals:                       97\n",
      "Model Family:        NegativeBinomial   Df Model:                            2\n",
      "Link Function:                    Log   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -289.46\n",
      "Date:                Fri, 08 Nov 2024   Deviance:                       105.86\n",
      "Time:                        12:59:31   Pearson chi2:                     122.\n",
      "No. Iterations:                     6   Pseudo R-squ. (CS):             0.4757\n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept          -30.1292      4.580     -6.578      0.000     -39.106     -21.152\n",
      "publication_year     0.0197      0.003      6.967      0.000       0.014       0.025\n",
      "non_classics        -0.0002      0.000     -2.009      0.045      -0.000   -5.76e-06\n",
      "====================================================================================\n",
      "         mean    mean_se  mean_ci_lower  mean_ci_upper\n",
      "0   15.029121   2.171045      11.323287      19.947784\n",
      "1   16.681495   2.025418      13.148754      21.163396\n",
      "2   17.992142   2.046186      14.397218      22.484704\n",
      "3   19.688335   2.251775      15.734618      24.635522\n",
      "4   19.259865   2.217722      15.368789      24.136085\n",
      "..        ...        ...            ...            ...\n",
      "95  20.640326  15.263079       4.844749      87.935015\n",
      "96  30.546722  17.494218       9.942107      93.853575\n",
      "97  36.802460  18.494164      13.744426      98.543295\n",
      "98  30.375707  17.940590       9.545248      96.664178\n",
      "99  36.285528  18.990451      13.009101     101.209112\n",
      "\n",
      "[100 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "#Setup the regression expression in patsy notation. We are telling patsy that BB_COUNT is our dependent variable and it depends on the regression variables: DAY, DAY_OF_WEEK, MONTH, HIGH_T, LOW_T and PRECIP\n",
    "expr = \"\"\"estc_id ~ publication_year+non_classics   \"\"\"\n",
    "\n",
    "#Set up the X and y matrices for the training and testing data sets\n",
    "y_train_other, X_train_other = dmatrices(expr, seventeeth_other, return_type='dataframe')\n",
    "y_test_other, X_test_other = dmatrices(expr, eighteenth_other, return_type='dataframe')\n",
    "\n",
    "#Using the statsmodels GLM class, train the Poisson regression model on the training data set\n",
    "poisson_training_results_other = sm.GLM(y_train_other, X_train_other, family=sm.families.Poisson()).fit()\n",
    "\n",
    "#print out the training summary\n",
    "print(poisson_training_results_other.summary())\n",
    "\n",
    "#print out the fitted rate vector\n",
    "print(poisson_training_results_other.mu)\n",
    "\n",
    "#Add the λ vector as a new column called 'BB_LAMBDA' to the Data Frame of the training data set\n",
    "seventeeth_other['BB_LAMBDA'] = poisson_training_results_other.mu\n",
    "\n",
    "#add a derived column called 'AUX_OLS_DEP' to the pandas Data Frame. This new column will store the values of the dependent variable of the OLS regression\n",
    "seventeeth_other['AUX_OLS_DEP'] = seventeeth_other.apply(lambda x: ((x['estc_id'] - x['BB_LAMBDA'])**2 - x['BB_LAMBDA']) / x['BB_LAMBDA'], axis=1)\n",
    "\n",
    "#use patsy to form the model specification for the OLSR\n",
    "ols_expr = \"\"\"AUX_OLS_DEP ~ BB_LAMBDA - 1\"\"\"\n",
    "\n",
    "#Configure and fit the OLSR model\n",
    "aux_olsr_results_other = smf.ols(ols_expr, seventeeth_other).fit()\n",
    "\n",
    "#Print the regression params\n",
    "print(aux_olsr_results_other.params)\n",
    "\n",
    "#train the NB2 model on the training data set\n",
    "nb2_training_results_other = sm.GLM(y_train_other, X_train_other,family=sm.families.NegativeBinomial(alpha=aux_olsr_results_other.params[0])).fit()\n",
    "\n",
    "#print the training summary\n",
    "print(nb2_training_results_other.summary())\n",
    "\n",
    "#make some predictions using our trained NB2 model\n",
    "nb2_predictions_other = nb2_training_results_other.get_prediction(X_test_other)\n",
    "\n",
    "#print out the predictions\n",
    "predictions_summary_frame_other = nb2_predictions_other.summary_frame()\n",
    "print(predictions_summary_frame_other)\n",
    "\n",
    "#plot the predicted counts versus the actual counts for the test data\n",
    "predicted_counts_other=predictions_summary_frame_other['mean']\n",
    "actual_counts_other = y_test_other['estc_id']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "525e701d",
   "metadata": {},
   "outputs": [],
   "source": [
    "MAE_frequent=mean_absolute_error(eighteenth_frequent['estc_id'], predicted_counts_freq)\n",
    "RMSE_frequent=mean_squared_error(eighteenth_frequent['estc_id'], predicted_counts_freq, squared=False)\n",
    "MAE_other=mean_absolute_error(eighteenth_other['estc_id'], predicted_counts_other)\n",
    "RMSE_other=mean_squared_error(eighteenth_other['estc_id'], predicted_counts_other, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5af5440",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequent Category:\n",
      "MAE: 10.152022332803565\n",
      "RMSE: 12.90929524997402\n",
      "\n",
      "Other Category:\n",
      "MAE: 23.94751466498858\n",
      "RMSE: 27.48642168555176\n"
     ]
    }
   ],
   "source": [
    "print(\"Frequent Category:\")\n",
    "print(\"MAE:\", MAE_frequent)\n",
    "print(\"RMSE:\", RMSE_frequent)\n",
    "\n",
    "print(\"\\nOther Category:\")\n",
    "print(\"MAE:\", MAE_other)\n",
    "print(\"RMSE:\", RMSE_other)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5718a37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Ohp37tzByJEjIRKJAACjRo2CQCDA5s2bJcsdP34cxcXFGD9+vMrr1lRWVhamT5+OBg0awMDAAAYGBrCwsEB2dnapEm9tu3DhAnJzc0uVAbu4uKBz5844efKkxuv+8MMPJccYAHx9fQFApbL5irzHipw6dQrm5uYYNGiQ3OP8fpfcT37kalV5enriypUrcn8LFy6UW0Yfn31lVP1M8wQCAfr06SP3mK+vr8ZdIHi///472rdvDwsLCxgYGMDQ0BBhYWEKz/tOnTrB0tJS8u+6deuiTp06kjZkZ2fjypUrGDhwoNz3nKWlZam2VwRjTOWR0IHyPwd37tzBf//9J3l/Zd+PXr164fHjx7h165bcOkueS2UJDw+HWCyW65oxevRoZGdnY8+ePSqvRx0ODg5o06aN3GMlz5dTp07Bx8en1HIhISFgjOHUqVNyj/ft2xeGhoZyj7Vp0wbXr1/HuHHjcPToUWRkZGh5TwghFUVBNyFEq7Zu3YorV67g33//xaNHj3Djxg20b99ebhkzMzNYWVnJPfb06VOkp6fDyMgIhoaGcn9PnjzBixcvAAAvX74EwP2YkWVgYCAJRpXh+4dqOlAYP8p369atS7Vxz5495bZR2WOKjB49Gnl5edi9ezcALjh4/PgxRo0aJVmmY8eO2LdvnyRocHZ2RtOmTbFr165y18/3xRwwYADS09ORnp4Oa2trdOjQAX/++adk5PaKHjN1DBs2DOvWrcOYMWNw9OhRXL58GVeuXIG9vT1yc3PVXl/9+vWRnJys0rL8e1avXr1Szzk6Okqe10TJ85If9EiVfarIe6zIy5cvJVPEyapTpw4MDAxK7aei41EWExMT+Pn5yf25u7tLntfXZ18ZVT/Tsu0vecHO2NgYeXl5Gm0fAPbu3YvBgwfDyckJ27dvx4ULF3DlyhXJd0BJivbV2NhYcj69fv0aYrFY4++f+vXrA0CZn53s7Gy8ePECLi4u5a6PV97ngH8vpk6dWuq9GDduHACUej9UPT/58SocHR3RqlUryXde165dYW5uXqGZG8pS3nsFcOe1su8d/nlZipadOXMmVq5ciYsXL6Jnz56ws7NDly5dEBsbW9FdIIRoCY1eTgjRKm9vb8no5cooyo7Url0bdnZ2OHLkiMLX8Jkd/kfMkydP4OTkJHm+qKio3MCIH+DowYMHZS6nTO3atQEAf/zxB1xdXZUuJ9vGkhQ9pgif+QgPD8cXX3yB8PBwODo6onv37nLL9evXD/369UN+fj4uXryIpUuXYtiwYXBzc4O/v7/Cdb958wZ//vknAGkmtaSdO3di3LhxcsdMnR/YPD5AKTnAUckfz2/evMGBAwcwd+5czJgxQ/J4fn4+Xr16pfZ2ASAoKAg//vgjLl68WO6I2vx7pmje3kePHknee4DbJ74CQVbJfdIWTd5jZezs7HDp0qVSWcpnz56hqKhIbj8BxZ/Viqisz77seSc7snPJ90jVz7Qubd++He7u7tizZ4/c8SlvUDBlbG1tIRAINP7+adWqFWxtbfHXX39h6dKlCt+zv/76C2KxGN26ddOojYrw78XMmTNLDXDI8/Lykvu3qufniRMnJNllRYHwxYsXkZCQAB8fHwBcYKzo+Ffk4psydnZ2Sr93AKj0mTQwMMDkyZMxefJkpKen48SJE/j2228RFBSE+/fv0wwBhFQBlOkmhFQJvXv3xsuXL1FcXFwqU+bn5yf5scWPGr1jxw651//2228oKioqcxuNGjWCp6cnNm/eXOYPWmWZyKCgIBgYGODu3bsK28hfbPDy8kK9evWwa9cuMMYkr09NTcX58+dVOyDgyr0vXbqEs2fP4u+//5YrBVfU5oCAACxfvhwAFI6mztu5cydyc3OxcOFCnD59utRf7dq1JSXm3bt3h0gkwvr168tsa8nsDY8fzfvGjRtyj//1119y/xYIBGCMlZr6ZtOmTSguLi5z28p88803MDc3x7hx4xQGyYwxyZRh/v7+MDU1xfbt2+WWefDgAU6dOiUZZZvfp9u3b8udQy9fvlTrvS1Jley3Ou+xMl26dEFWVhb27dsn9zg/sr7sflYWXXz2lZ13f//9t9y/Vf1Mq0OdSgaAO/eNjIzkgqknT54oHL1cFebm5mjTpg327t0rlynPzMwstf+KGBkZYdq0aUhMTMSKFStKPf/s2TPMnDkTdevWxZgxYySPK/sOUJWXlxcaNmyI69evK30vZMvq1REWFgahUIh9+/aV+r7btm0bAMh1q3Fzcyt17pw6dQpZWVlyj6n7XivSpUsXJCQk4J9//pF7fOvWrRAIBOjUqZNa67OxscGgQYMwfvx4vHr1iuYQJ6SKoEw3IaRKGDp0KHbs2IFevXph4sSJaNOmDQwNDfHgwQOcPn0a/fr1w4ABA+Dt7Y3hw4djzZo1MDQ0RNeuXREXF4eVK1eWKltV5KeffkKfPn3w/vvv45tvvkH9+vWRlpaGo0ePSn7MN2vWDACwdu1ajBw5EoaGhvDy8oKbmxsWLFiAWbNm4d69e+jRowdsbW3x9OlTXL58Gebm5pg/fz6EQiEWLlyIMWPGYMCAARg7dizS09Mxb948lcvLASA4OBiTJ09GcHAw8vPzS/U3njNnDh48eIAuXbrA2dkZ6enpWLt2LQwNDREQEKB0vWFhYbC1tcXUqVMV9m0fMWIEVq9ejevXr+O9997Dt99+i4ULFyI3NxfBwcGwtrZGQkICXrx4gfnz50uO2d69e7F+/Xq0atUKQqEQfn5+cHBwQNeuXbF06VLY2trC1dUVJ0+exN69e+W2aWVlhY4dO2LFihWoXbs23NzcEB0djbCwMI3nAXZ3d8fu3bsxZMgQNG/eHBMmTECLFi0AAAkJCdi8eTMYYxgwYABsbGwwe/ZsfPvttxgxYgSCg4Px8uVLzJ8/HyYmJpg7d65kvZ9++ik2btyI4cOHY+zYsXj58iW+++47lc4/Zfhzbvny5ejZsydEIhF8fX2xaNEijd5jZUaMGIGffvoJI0eOREpKCpo1a4azZ89iyZIl6NWrF7p27arxPmhKF5/9Xr16oVatWvjss8+wYMECGBgYICIiAvfv35dbTtXPtDosLS3h6uqK/fv3o0uXLqhVq5bknFakd+/e2Lt3L8aNG4dBgwbh/v37WLhwIerVq4ekpCS1ts1buHAhevTogW7dumHKlCkoLi7G8uXLYW5urlLlyPTp03H9+nXJ7ZAhQ2BtbY0bN25gxYoVyMzMxIEDB+Sm4lP2HaCOjRs3omfPnggKCkJISAicnJzw6tUrJCYm4p9//sHvv/+u9rF4+fIl9u/fj6CgIPTr10/hMt9//z22bt2KpUuXwtDQEJ9++ilmz56NOXPmICAgAAkJCVi3bl2pqQebNm0KAPjll19gaWkJExMTuLu7q9Xd4ZtvvsHWrVvx4YcfYsGCBXB1dcXBgwfx888/46uvvkKjRo3KXUefPn3QtGlT+Pn5wd7eHqmpqVizZg1cXV3RsGFDldtCCNEhPQ7iRgipQZRNGVbSyJEjmbm5ucLnCgsL2cqVK9l7773HTExMmIWFBWvcuDH74osvWFJSkmS5/Px8NmXKFFanTh3JCNUXLlwoNbKsslGuL1y4wHr27Mmsra2ZsbEx8/T0LDUi8syZM5mjoyMTCoWl1rFv3z7WqVMnZmVlxYyNjZmrqysbNGgQO3HihNw6Nm3axBo2bMiMjIxYo0aN2ObNm0uNcF2eYcOGMQCsffv2pZ47cOAA69mzJ3NycmJGRkasTp06rFevXuzMmTNK13f9+nUGgE2aNEnpMv/99x8DIDei89atW1nr1q0l70uLFi3kRh5/9eoVGzRoELOxsWECgUBuhOTHjx+zQYMGsVq1ajFra2s2fPhwFhsbW2r08gcPHrCPPvqI2draMktLS9ajRw8WFxen8vuqzN27d9m4ceNYgwYNmLGxMTM1NWU+Pj5s8uTJpUao37RpE/P19WVGRkbM2tqa9evXT27aKN6WLVuYt7c3MzExYT4+PmzPnj1KRy9fsWJFqdejxOjI+fn5bMyYMcze3l5y/JKTkzV6j3mKRi9njLGXL1+yL7/8ktWrV48ZGBgwV1dXNnPmTJaXl1eqjePHjy93O+Vtj1eZn33GuNGx27Vrx8zNzZmTkxObO3cu27Rpk8KZCVT5TCtrv6KR0k+cOMFatGjBjI2NS42srsiyZcuYm5sbMzY2Zt7e3uzXX39VuF5l74mi/f/rr78k53L9+vXZsmXLlI7qrohYLGY7duxggYGBzMbGhhkZGTF3d3f21VdfsdTU1FLLK/sOUOdzwBj3HTV48GBWp04dZmhoyBwcHFjnzp3lRlNX9f8bxhhbs2YNA8D27dundBl+VH9+BP38/HwWGhrKXFxcmKmpKQsICGDXrl1TeJzXrFnD3N3dmUgkkvtOU/Z5UPR/QGpqKhs2bBizs7NjhoaGzMvLi61YsUIyWjtjZR/HVatWsXbt2rHatWtL3u/PPvuMpaSklHt8CCGVQ8CYTO0jIYQQQkg14+bmhsDAQEREROi7KYQQQkgp1KebEEIIIYQQQgjREQq6CSGEEEIIIYQQHaHyckIIIYQQQgghREco000IIYQQQgghhOgIBd2EEEIIIYQQQoiOUNBNCCGEEEIIIYToiIG+G6BrYrEYjx49gqWlJQQCgb6bQwghhBBCCCGkBmCMITMzE46OjhAKleeza3zQ/ejRI7i4uOi7GYQQQgghhBBCaqD79+/D2dlZ6fM1Pui2tLQEwB0IKysrPbdGucLCQhw7dgzdu3eHoaGhvptDSCl0jpLqgM5TUh3QeUqqOjpHSXVQFc7TjIwMuLi4SGJOZWp80M2XlFtZWVX5oNvMzAxWVlb05UaqJDpHSXVA5ympDug8JVUdnaOkOqhK52l53ZhpIDVCCCGEEEIIIURHKOgmhBBCCCGEEEJ0hIJuQgghhBBCCCFER2p8n25VFRcXo7CwUG/bLywshIGBAfLy8lBcXKy3dhCiTHU9R42MjMqcwoEQQgghhBBdeueDbsYYnjx5gvT0dL23w8HBAffv36f5xEmVVF3PUaFQCHd3dxgZGem7KYQQQggh5B30zgfdfMBdp04dmJmZ6S2YEIvFyMrKgoWFBWXlSJVUHc9RsViMR48e4fHjx6hfv361ulhACCGEEEJqhnc66C4uLpYE3HZ2dnpti1gsRkFBAUxMTKpNQEPeLdX1HLW3t8ejR49QVFSk9+kkCCGEEELIu6f6/HLWAb4Pt5mZmZ5bQgjRFb6svDr1QyeEEEIIITXHOx1086jklJCaiz7fhBBCCCFEnyjoJoQQQgghhBBCdISCbkIUiIqKgkAg0Puo9ryXL1+iTp06SElJ0XdTVDJv3jw0b95c8u+pU6fi66+/1l+DCCGEEEII0RMKuquhkJAQCASCUn937tzRd9O0ws3NDWvWrFF5+SVLlkAkEmHZsmUabS8wMBCTJk3S6LWVZenSpejTpw/c3NxKPde9e3eIRCJcvHhRrXVGRETAxsZGOw0sR2hoKMLDw5GcnFwp2yOEEEIIIaSqoKBbC8SMITWzAAmv8pGaWQAxYzrfZo8ePfD48WO5P3d391LLFRQU6Lwt+hYeHo7Q0FBs3rxZ300pFz94nzpyc3MRFhaGMWPGlHouLS0NFy5cwIQJExAWFqaNJupEnTp10L17d2zYsEHfTSGEEEIIIaRSUdBdQbfS87E+/jV23cnAX6mZ2HUnA+vjX+NWer5Ot2tsbAwHBwe5P5FIhMDAQEyYMAGTJ09G7dq10a1bNwBAQkICevXqBQsLC9StWxeffvopXrx4IVlfdnY2RowYAQsLC9SrVw+rVq0qlQEWCATYt2+fXDtsbGwQEREh+ffDhw8xZMgQ2Nraws7ODv369ZMriQ4JCUH//v2xcuVK1KtXD3Z2dhg/frwkGA0MDERqaiq++eYbSQa/LNHR0cjNzcWCBQuQnZ2NmJgYuef57cmaNGkSAgMDJc9HR0dj7dq1ku3Jtvfq1avw8/ODmZkZ2rVrh1u3bsmta/369fD09ISRkRG8vLywbds2uecFAgE2bNiAfv36wdzcHIsWLcLr16/xySefwN7eHqampmjYsCHCw8OV7uPhw4dhYGAAf3//Us+Fh4ejd+/e+Oqrr7Bnzx5kZ2fLPZ+eno7PP/8cdevWhYmJCZo2bYoDBw4gKioKo0aNwps3byT7PW/ePEmby3ufp0+fjkaNGsHMzAweHh6YPXt2uRcU+vbti127dpW5DCGEEEIIITUNBd0VcCs9H5HJmcgsFMs9nlkoRmRyps4Db2W2bNkCAwMDnDt3Dhs3bsTjx48REBCA5s2bIzY2FkeOHMHTp08xePBgyWumTZuG06dPIzIyEseOHUNUVBSuXr2q1nZzcnLQqVMnWFhYICYmBmfPnoWFhQV69Oghl3E/ffo07t69i9OnT2PLli2IiIiQBHR79+6Fs7MzFixYIMnglyUsLAzBwcEwNDREcHCw2tnetWvXwt/fH2PHjpVsz8XFRfL8rFmzsGrVKsTGxsLAwACjR4+WPBcZGYmJEydiypQpiIuLwxdffIFRo0bh9OnTctuYO3cu+vXrh5s3b2L06NGYPXs2EhIScPjwYSQmJmL9+vWoXbu20jbGxMTAz8+v1OOMMYSHh2P48OFo3LgxGjVqhN9++03yvFgsRs+ePXH+/Hls374dCQkJWLZsGUQiEdq1a4c1a9bAyspKst9Tp05V+bhZWloiIiICCQkJWLt2LX799Vd8//33Zb6mTZs2uH//PlJTU1XeDiGEEEIIIdWdgb4bUF2JGcOJB9llLnPiQTYaWhtBqIMpiw4cOAALCwvJv3v27Inff/8dANCgQQN89913kufmzJmDli1bYsmSJZLHNm/eDBcXF9y+fRuOjo4ICwvD1q1bJZnxLVu2wNnZWa027d69G0KhEJs2bZJkqMPDw2FjY4OoqCh0794dAGBra4t169ZBJBKhcePG+PDDD3Hy5EmMHTsWtWrVgkgkgqWlJRwcHMrcXkZGBv7880+cP38eADB8+HC0b98eP/74I6ysrFRqs7W1NYyMjGBmZqZwe4sXL0ZAQAAAYMaMGfjwww+Rl5cHExMTrFy5EiEhIRg3bhwAYPLkybh48SJWrlyJTp06SdYxbNgwuWA9LS0NLVq0kATSivppy0pJSYGjo2Opx0+cOIGcnBwEBQVJ9j8sLAyjRo2SPH/58mUkJiaiUaNGAAAPDw+5fRcIBOUeZ0X+7//+T3Lfzc0NU6ZMwZ49exAaGqr0NU5OTpL9cXV1VXubhBBCCCGEVEeU6dbQ/azCUhnukjILxbifpX4fXlV06tQJ165dk/z98MMPkudKZkWvXr2K06dPw8LCQvLXuHFjAMDdu3dx9+5dFBQUyJUv16pVC15eXmq16erVq7hz5w4sLS0l26lVqxby8vJw9+5dyXJNmjSBSCSS/LtevXp49uyZWtsCgJ07d8LDwwPvvfceAKB58+bw8PDA7t271V6XMr6+vnLtBCBpa2JiItq3by+3fPv27ZGYmCj3WMn346uvvsLu3bvRvHlzhIaGSi4aKJObmwsTE5NSj4eFhWHIkCEwMOCunQUHB+PSpUuSEvhr167B2dlZEnBr0x9//IEOHTrAwcEBFhYWmD17NtLS0sp8jampKQCuIoIQQgghhJB3BWW6NZRdqNpgaaoupy5zc3M0aNBA6XOyxGIx+vTpg+XLl5datl69ekhKSlJpmwKBAKzEIHGy/XjFYjFatWqFHTt2lHqtvb295L6hoWGp9YrFZV/AUGTz5s2Ij4+XBJ18G8LCwvD5558DAIRCYZltLo9sW/nsvWxbS/Y5Z4yVeqzk+9GzZ0+kpqbi4MGDOHHiBLp06YLx48dj5cqVCttQu3ZtvH79Wu6xV69eYd++fSgsLMT69esljxcXF2Pz5s1Yvny5JMhVV3nv88WLFzF06FDMnz8fQUFBsLa2xu7du7Fq1aoy1/vq1SsA8ucCIYQQQgghNR0F3RoyN1StZFzV5XSpZcuW+PPPP+Hm5iYXoPIaNGgAQ0NDXLx4EfXr1wcAvH79Grdv35aUVgNcsCTbxzopKUkua9myZUvs2bMHderUUbm8WxEjIyMUFxeXuczNmzcRGxuLqKgo1KpVS/J4eno6OnbsiLi4ODRt2hT29vaIi4uTe+21a9fkgmlVtqeIt7c3zp49ixEjRkgeO3/+PLy9vct9rb29PUJCQhASEoIPPvgA06ZNUxp0t2jRAtu3b5d7bMeOHXB2di414NnJkyexdOlSLF68GL6+vnjw4AFu376tMNutbL/Le5/Pnz8PV1dXzJo1S/KYKv204+LiYGhoiCZNmpS7LCGEEEIIITUFlZdryMXCEJaGZR8+S0MhXCwMy1ymMowfPx6vXr1CcHAwLl++jHv37uHYsWMYPXo0iouLYWFhgc8++wzTpk3DyZMnERcXh5CQEAiF8vvXuXNnrFu3Dv/88w9iY2Px5ZdfygWvn3zyCWrXro1+/frhzJkzSE5ORnR0NCZOnIgHDx6o3F43NzfExMTg4cOHciOsywoLC0ObNm3QsWNHNG3aVPLXoUMH+Pv7SwZU69y5M2JjY7F161YkJSVh7ty5pYJwNzc3XLp0CSkpKXjx4oXKWfdp06YhIiICGzZsQFJSElavXo29e/eWOyDZnDlzsH//fty5cwfx8fE4cOBAmYF6UFAQ4uPj5bLdYWFhGDRokNy+N23aFKNHj0Z6ejoOHjyIgIAAdOzYER999BGOHz+O5ORkHD58GEeOHJHsd1ZWFk6ePIkXL15IAuvy3mdPT0+kpaVh9+7duHv3Ln744QdERkaWe7zOnDmDDz74QOMMPCGEEEKqiDt3gC+/BEr8piKEKEZBt4aEAgG6OpuXuUxXZ3OdDKKmLkdHR5w7dw7FxcUICgpC06ZNMXHiRFhbW0sC6xUrVqBjx47o27cvunbtig4dOqBVq1Zy61m1ahVcXFzQsWNHDBs2DFOnToWZmZnkeTMzM8TExKB+/foYOHAgvL29MXr0aOTm5qqV+V6wYAFSUlLg6empsBS5oKAA27dvx0cffaTw9R999BG2b9+OgoICBAUFYfbs2QgNDUXr1q2RmZkpl5kGgKlTp0IkEsHHxwf29vbl9k3m9e/fH2vXrsWKFSvQpEkTbNy4EeHh4ZLpyJQxMjLCzJkz4evri44dO0IkEpXZD71Zs2bw8/OTjEx+9epVXL9+XeH+W1paonv37pKLDn/++Sdat26N4OBg+Pj4IDQ0VJLdbteuHb788ksMGTIE9vb2ksH3ynuf+/Xrh2+++QYTJkxA8+bNcf78ecyePbvc47Vr1y6MHTu23OUIIYQQUsVt3Mj9BQYCN2/quzWEVHkCVrLzZg2TkZEBa2trvHnzplTgl5eXh+TkZLi7uyscqEoVt9LzceJBttygapaGQnR1NoeXjbHK6xGLxcjIyICVlVWpDLO+BAYGonnz5lizZo2+m/LOO3ToEKZOnYq4uDi9nR8VOUcPHjyIadOm4caNGwq7OOiSNj7npPooLCzEoUOH0KtXr1LjRxBSVdB5Sqq6cs/RsWOBTZu4+/b2QFQU4ONTqW0kpCp8l5YVa8qiPt0V5GVjjIbWRrifVYjsQgZzQwFcLAyrRIab1By9evVCUlISHj58KDePeHWRnZ2N8PDwSg+4CSGEEKID2W+nzTUwAJ4/Bzp3BqKjATVnviHkXUG/gLVAKBDA1dJI380gNdzEiRP13QSNDR48WN9NIIQQQoi28EH3woXArl3AjRvSwFvJ7DqEvMso6CZKRUVF6bsJhBBCCCGkquGD7vr1gRMngE6dgPh4aeDt7q7f9hFSxVSNzsOEEEIIIYSQ6oEPus3NuT7dJ08CjRsD9+9zAbgKU4kS8i6hoJsQQgghhBCiOj7o5mc3qVsXOHUKaNiQC7g7dwbUmC6WkJqOgm5CCCGEEEKI6mQz3bx69bjA28MDuHePC7wfPdJP+wipYijoJoQQQgghhKguJ4e7lQ26AcDZmQu8XV2BpCTgww+Bmj07MSEqoaCbEEIIIYQQojpFmW6eqytw+jRgYgJcuwYkJlZq0wipiijoJoQQQgghhKiGsbKDboAbvbxDB+7+qVOV0y5CqjAKuglRICoqCgKBAOnp6fpuCgDg5cuXqFOnDlJSUvSyfYFAgH379gEAUlJSIBAIcO3atUpvR0hICPr37y/596BBg7B69epKbwchhBDyzsrPB8Ri7r6yoBvg+nQDFHQTAgq6q6WQkBAIBIJSf3fu3NF307TCzc0Na9asUXn5JUuWQCQSYdmyZRptLzAwEJMmTdLotZVl6dKl6NOnD9zc3ABIA1/+z9bWFh07dkR0dLTO2+Li4oLHjx+jadOmKi1fMlDWpjlz5mDx4sXIyMjQyfoJIYQQUgKf5QbKDro7deJuo6KkQToh7yi9B90PHz7E8OHDYWdnBzMzMzRv3hxXr16VPM8Yw7x58+Do6AhTU1MEBgYiPj5ejy2uGnr06IHHjx/L/bm7u5darqCgQA+tq1zh4eEIDQ3F5s2b9d2UchUWFqr9mtzcXISFhWHMmDGlnjtx4gQeP36M6OhoWFlZoVevXkhOTtbathURiURwcHCAgYGBVtZXEb6+vnBzc8OOHTv03RRCCCHk3cAH3cbGgEikfDk/P8DSEnj9Grh+vXLaRkgVpdeg+/Xr12jfvj0MDQ1x+PBhJCQkYNWqVbCxsZEs891332H16tVYt24drly5AgcHB3Tr1g2ZmZn6a3gVYGxsDAcHB7k/kUiEwMBATJgwAZMnT0bt2rXRrVs3AEBCQgJ69eoFCwsL1K1bF59++ilevHghWV92djZGjBgBCwsL1KtXD6tWrSqVAZYtMebZ2NggIiJC8u+HDx9iyJAhsLW1hZ2dHfr16ydXEs1nPVeuXIl69erBzs4O48ePlwSEgYGBSE1NxTfffCPJ4pYlOjoaubm5WLBgAbKzsxETEyP3vKIs66RJkxAYGCh5Pjo6GmvXrpVsT7a9V69ehZ+fH8zMzNCuXTvcunVLbl3r16+Hp6cnjIyM4OXlhW3btsk9LxAIsGHDBvTr1w/m5uZYtGgRXr9+jU8++QT29vYwNTVFw4YNER4ernQfDx8+DAMDA/j7+5d6zs7ODg4ODvD19cXGjRuRk5ODY8eOKd02APz9999o1aoVTExM4OHhgfnz56OoqEiyzqSkJHTs2BEmJibw8fHB8ePH5bapqLw8Pj4eH374IaysrGBpaYkPPvgAd+/exbx587Blyxbs379fcnyjoqIAlH+uFBcXY/LkybCxsYGdnR1CQ0PBFIyA2rdvX+zatUvp8SOEEEKIFpXXn5tnYAB07MjdpxJz8o7Ta9C9fPlyuLi4IDw8HG3atIGbmxu6dOkCT09PAFyWe82aNZg1axYGDhyIpk2bYsuWLcjJycHOnTt10ibGGLKzCyr9T1EwoaktW7bAwMAA586dw8aNG/H48WMEBASgefPmiI2NxZEjR/D06VMMHjxY8ppp06bh9OnTiIyMxLFjxxAVFSVXcaCKnJwcdOrUCRYWFoiJicHZs2dhYWGBHj16yGXcT58+jbt37+L06dPYsmULIiIiJIH73r174ezsjAULFkgy+GUJCwtDcHAwDA0NERwcjLCwMLXavHbtWvj7+2Ps2LGS7bm4uEienzVrFlatWoXY2FgYGBhg9OjRkuciIyMxceJETJkyBXFxcfjiiy8watQonD59Wm4bc+fORb9+/XDz5k2MHj0as2fPRkJCAg4fPozExESsX78etWvXVtrGmJgY+Pn5lbsvZmZmAOQz2iW3ffToUQwfPhxff/01EhISsHHjRkRERGDx4sUAALFYjIEDB0IkEuHixYvYsGEDpk+fXuZ2Hz58KAnST506hatXr2L06NEoKirC1KlTMXjwYLnKjHbt2ql0rqxatQqbN29GWFgYzp49i1evXiEyMrLU9tu0aYPLly8jPz+/3GNECCGEkApSNegGpCXmJX4bEfKu0Wt96F9//YWgoCB8/PHHiI6OhpOTE8aNG4exY8cCAJKTk/HkyRN0795d8hpjY2MEBATg/Pnz+OKLL7TeppycQlhYLNX6esuTkVF2YFPSgQMHYGFhIfl3z5498fvvvwMAGjRogO+++07y3Jw5c9CyZUssWbJE8tjmzZvh4uKC27dvw9HREWFhYdi6daskM75lyxY4Ozur1abdu3dDKBRi06ZNkgx1eHg4bGxsEBUVJXkfbW1tsW7dOohEIjRu3BgffvghTp48ibFjx6JWrVoQiUSwtLSEg4NDmdvLyMjAn3/+ifPnzwMAhg8fjvbt2+PHH3+ElZWVSm22traGkZERzMzMFG5v8eLFCAgIAADMmDEDH374IfLy8mBiYoKVK1ciJCQE48aNAwBMnjwZFy9exMqVK9GJ/08GwLBhw+SC9bS0NLRo0UISSPP9tJVJSUmBo6NjmctkZ2dj5syZEIlEkvYq2vann36KGTNmYOTIkQAADw8PLFy4EKGhoZg7dy5OnDiBxMREpKSkSN7/JUuWoGfPnkq3/dNPP8Ha2hq7d++GoaEhAKBRo0aS501NTZGfny93fLdv317uubJmzRrMnDkTH330EQBgw4YNOHr0aKntOzk5IT8/H0+ePIGrq2uZx4kQQgghFaRO0M0PphYdDRQWAm9/JxDyrtFr0H3v3j2sX78ekydPxrfffovLly/j66+/hrGxMUaMGIEnT54AAOrWrSv3urp16yI1NVXhOvPz8+UyXvwAS4WFhaX6tBYWFoIxBrFYDPHbAR7Eehrogc908+0pb9nAwED8/PPPksfMzc0lr2vVqpXcOmJjY3H69Gm5IJ2XlJSE7OxsFBQUoG3btpLX2djYwMvLq1R7ZI9VycdiY2Nx584dWFpayj2fl5eHpKQkdO3aFYwx+Pj4QCAQSNbj4OCAuLg4ufWqchy2b98ODw8PNGvWDGKxGL6+vvDw8MDOnTvx+eefS9ZTcl38sS5re/z9pk2bSu7z5+GTJ09Qv359JCYmYsyYMXKva9euHX744Qe5x1q2bCn37y+++AIff/wx/vnnH3Tr1g39+vVDu3btlO5nTk4OHB0dFbavXbt2EAqFyMnJQb169bB582Y0adJE8nzJbV+9ehVXrlyRZLYBrow7Ly8PWVlZSEhIQP369eW217ZtW7n2yB4/sViMf//9Fx06dIBIJFL4nil6D8o7V1q3bo3Hjx/LnZNCoRCtWrUqtS5jY2MAQFZWlsLti8ViMMZQWFgIUVl9z0iNwH/Pa2sMA0J0gc5TUtWVdY4K3ryBAQCxmRmKyzuHfXxgYGsLwevXKLp0CazEbwpCKqIqfJequm29Bt1isRh+fn6SDGyLFi0QHx+P9evXY8SIEZLlSvbrZYwp7eu7dOlSzJ8/v9Tjx44dk5Tf8gwMDODg4ICsrCxJSStjDA8ejKvQfmmiuDgPAoFApb7qhYWFMDY2Rp06deQez8jIQFFREQwNDeVGcy4oKECPHj0wb968UuuqW7cu7t27BwDIzMyUe11xcTEKCgokjwkEAuTk5MgtU1hYiLy8PGRkZCAvLw/NmzfHL7/8Umo7dnZ2yMjIQGFhIQQCQal1yG5HLBZL1lmWsLAwxMfHw8jISPKYWCzGpk2bMHToUMk+FBYWyq0rOzsbRUVFkseKiorktg9wgS4AuXbwj2VkZCAjIwOMMeTn58u9Ljc3F4wxuceEQqHcv9u3b48bN25Iyvi7deuGMWPGYOHChQr309raGs+ePZNbR1ZWluQYNG7cGNbW1qhVq5akfcq2LRaLMWPGDPTp06fUdgoKCpCbmwuxWCz3Gv6czM3NlRw//jYjIwOGhoaljrGswsJCueMNQKVzRXYbvKKiolLH9/79+wAAExMThW3g9ysmJkau7zqp2UqORUBIVUTnKanqFJ2jjufOoTWAV/n5OHfoULnraO3lBceLF3F740YkvXypg1aSd50+v0v5+KA8eg2669WrBx8fH7nHvL298eeffwKApBz1yZMnqFevnmSZZ8+elcp+82bOnInJkydL/p2RkQEXFxd07969VMlxXl4e7t+/DwsLC5iYmEget7au2H5pgjGGzMxMWFpaljt4mKGhIQwMDBSWUBsYGMDIyEjuuTZt2mDv3r1o2rSpwhGnLS0tYWhoiPj4eDRp0gQAN8jd3bt30alTJ8m67O3t8ebNG8m/k5KSkJOTAxMTE1hZWaFt27bYt28fPDw8lJZ3K2q7kZGR3GMmJiYwNDQss0T85s2b+Pfff3Hq1ClJsAkA6enpCAwMRFpaGpo2bQpHR0fcvn1bbl2JiYly6zc1NYVIJJJbhr9AY2lpKXnc/G0ZlYWFBaysrODj44OrV69KsuoA8M8//8DHx0duXaampqX2xcrKCl9++SW+/PJLbNy4EdOnT8fatWsV7mubNm2wY8cOuXXwVQteXl547733lB6nkttu2bIlUlNT0bx5c4XLt2jRAg8ePEBWVpakpP3ChQuSdckeB3Nzc1hZWaFly5bYunUrTE1NJeXlsszNzZGRkSHXDlXOlXr16iEuLk5S2l5UVIQbN26gRYsWcq9JTk6Gs7OzwtH7Ae5zbmpqKul3Tmq2wsJCHD9+HN26dVN4PhJSFdB5Sqq6ss5RwduBeGs5O6NXr17lrkuYkgJcvIjGjx+joQrLE6KqqvBdquq0tXoNutu3b19qNOjbt29L+mW6u7vDwcEBx48fR4sWLQBwWavo6GgsX75c4TqNjY0l5aayDA0NS70ZxcXFEAgEEAqFEAr1O3saXxbLt6cs/CjQypYr+dyECROwadMmfPLJJ5g2bRpq166NO3fuYPfu3fj1119hZWWFzz77DNOnT4e9vT3q1q2LWbNmQSgUyq2rc+fO+Omnn+Dv7w+xWIzp06fD0NBQcvw+/fRTrFq1CgMGDMCCBQvg7OyMtLQ07N27F9OmTYOzs7PCtvMXGfjH3NzccObMGQQHB8PY2FjhIGP84Hv8KOSy/P39ER4eju+//x5dunTBypUrsX37dvj7+2P79u2Ii4tDixYtJNtzd3fH5cuXkZaWBgsLC9SqVUvynOy5UfKxadOmYfDgwWjVqhW6dOmCv//+G5GRkThx4oTc/pU8v+bMmYNWrVqhSZMmyM/Px6FDh+Dt7a30/ezRowe+/fZbvHnzBra2tgrbooyibffu3Rv169fHxx9/DKFQiBs3buDmzZtYtGgRunfvDi8vL4SEhGDVqlXIyMjA7Nmz5dYp+34JhUL873//w7p16zBs2DDMnDkT1tbWuHjxItq0aQMvLy+4u7vj2LFjSEpKgp2dHaytrVU6VyZOnIjly5ejUaNG8Pb2xurVq5Genl7q/Dl37hy6d++u9Djw57Gi7wBSc9H7TaoDOk9JVafwHM3LAwAILSwgVOX87dqVW/7cOQjFYm6qMUK0SJ/fpapuV6+R5jfffIOLFy9iyZIluHPnDnbu3IlffvkF48ePB8D9uJ80aRKWLFmCyMhIxMXFISQkBGZmZhg2bJg+m16tODo64ty5cyguLkZQUBCaNm2KiRMnwtraWhKorFixAh07dkTfvn3RtWtXdOjQAa1atZJbz6pVq+Di4oKOHTti2LBhmDp1qlzJvpmZGWJiYlC/fn0MHDgQ3t7eGD16NHJzc1Ue2AwAFixYgJSUFHh6esLe3r7U8wUFBdi+fbtkgK2SPvroI2zfvh0FBQUICgrC7NmzERoaitatWyMzM1Ou6wIATJ06FSKRCD4+PrC3t0daWppK7ezfvz/Wrl2LFStWoEmTJti4cSPCw8MVXgiQZWRkhJkzZ8LX1xcdO3aESCTC7t27lS7frFkz+Pn54bffflOpXWUJCgrCgQMHcPz4cbRu3Rrvv/8+Vq9eLbnQJRQKERkZifz8fLRp0wZjxoyR6/+tiJ2dHU6dOoWsrCwEBASgVatW+PXXXyVfQmPHjoWXlxf8/Pxgb2+Pc+fOqXSuTJkyBSNGjEBISAj8/f1haWmJAQMGyG07Ly8PkZGRksEXCSGEEKJj6gykBgA+PkCdOlywfumS7tpFSFXG9Ozvv/9mTZs2ZcbGxqxx48bsl19+kXteLBazuXPnMgcHB2ZsbMw6duzIbt68qfL637x5wwCwN2/elHouNzeXJSQksNzc3ArvR0UVFxez169fs+LiYn03RSIgIIBNnDhR380gjLGDBw8yb29vvZ4fVfEcXbduHevWrVuZy1SlzznRvYKCArZv3z5WUFCg76YQohSdp6SqK/McnTuXMYCxr75SfYVDh3KvmTtXW00kpEp8l5YVa8rSa3k5APTu3Ru9e/dW+rxAIMC8efMUDgJGyLuiV69eSEpKwsOHD+XmEX/XGRoa4scff9R3MwghhJB3h7qZboCbr3v3buDUKYB+05N3kN6DbkKIaiZOnKjvJlQ5soPYEUIIIaQSaBJ08/N1X7wI5OQAJWYUIqSmo6CbKBUVFaXvJhBCCCGEkKpEk6Db0xNwcQHu3wfOnQO6ddNN2wipovQ7ZDchhBBCCCFEezIzgbfTeumEJkG3QMCVmANciTkh7xgKugkhhBBCCKkJGANatwY8PICrV3WzDU2CbkBaYn76tHbbQ0g1QEE3IYQQQgghNcHTp8CtW1y2u29f4OFD7W9D06Cbz3RfuQK8eaPdNhFSxVHQTQghhBBCSE1w+7b0/qNHQJ8+QFaWdrehadBdvz7Xt1ssBs6c0W6bCKniKOgmhBBCCCGkJuCD7vfeA+ztgX//BYYPB4qLtbcNTYNugErMyTuLgm5CCCGEEEJqgqQk7rZjR2D/fsDYmLudMUN729BG0E2DqZF3DAXd76CIiAjY2NjouxlaERUVBYFAgPT0dH03BQDw8uVL1KlTBykpKfpuikrmzZuH5s2bS/49depUfP311/prECGEEEI0x2e6GzYE/P2B8HDu3ytXAps2aWcbfNCtyVzbgYHc7bVrwMuX2mkPIdUABd3V1P379/HZZ5/B0dERRkZGcHV1xcSJE/GyxBeYm5sb1qxZo59GlrBkyRKIRCIsW7ZMo9cHBgZi0qRJ2m2Uli1duhR9+vSBm5tbqee6d+8OkUiEixcvqrXOyrxIEhoaivDwcCQnJ1fK9gghhBCiRXzQ3agRdxscDMybx93/6ivg5MmKb6MimW4HB8DHh7sfHV3xthBSTVDQrQ3iYuBpFJCyi7sVa7HfjAL37t2Dn58fbt++jV27duHOnTvYsGEDTp48CX9/f7x69Uqn21emsLCwzOfDw8MRGhqKzZs3V1KLNFfeviiSm5uLsLAwjBkzptRzaWlpuHDhAiZMmICwsDBtNFEn6tSpg+7du2PDhg36bgohhBBC1FFcDNy5w93ng24AmDMHGDYMKCoCBg0C/vtP820UFQEFBdx9TYJugErMyTuJgu6Kur8X+MsNONkJOD+Mu/3LjXtcR8aPHw8jIyMcO3YMAQEBqF+/Pnr27IkTJ07g4cOHmDVrFgAuM5yamopvvvkGAoEAAoFAbj1Hjx6Ft7c3LCws0KNHDzx+/Fju+fDwcHh7e8PExASNGzfGzz//LHkuJSUFAoEAv/32GwIDA2FiYoLt27crbXN0dDRyc3OxYMECZGdnIyYmRu75kJAQ9O/fX+6xSZMmIfBtGVJISAiio6Oxdu1ayb7IlnBfvXoVfn5+MDMzQ7t27XDr1i25da1fvx6enp4wMjKCl5cXtm3bJve8QCDAhg0b0K9fP5ibm2PRokV4/fo1PvnkE9jb28PU1BQNGzZEOF+mpcDhw4dhYGAAf3//Us+Fh4ejd+/e+Oqrr7Bnzx5k81eJ30pPT8fnn3+OunXrwsTEBE2bNsWBAwcQFRWFUaNG4c2bN5L9nvf2irVAIMC+ffvk1mNjY4OIiAjJv6dPn45GjRrBzMwMHh4emD17drkXFPr27Ytdu3aVuQwhhBBCqpi0NC4gNjLiRgrnCQRAWBjQrh2Qng707g28eKHZNnJypPc1Dbr5qcMo6CbvEAq6K+L+XuDMICDngfzjOQ+5x3UQeL969QpHjx7FuHHjYGpqKvecg4MDPvnkE+zZsweMMezduxfOzs5YsGABHj9+LBdU5+TkYOXKldi2bRtiYmKQlpaGqVOnSp7/9ddfMWvWLCxevBiJiYlYsmQJZs+ejS1btshtc/r06fj666+RmJiIoKAgpe0OCwtDcHAwDA0NERwcrHa2d+3atfD398fYsWMl++Li4iJ5ftasWVi1ahViY2NhYGCA0aNHS56LjIzExIkTMWXKFMTFxeGLL77AqFGjcLrEyJlz585Fv379cPPmTYwePRqzZ89GQkICDh8+jMTERKxfvx61a9dW2saYmBj4+fmVepwxhvDwcAwfPhyNGzdGo0aN8Ntvv0meF4vF6NmzJ86fP4/t27cjISEBy5Ytg0gkQrt27bBmzRpYWVlJ9lv2fSqPpaUlIiIikJCQgLVr1+LXX3/F999/X+Zr2rRpg/v37yM1NVXl7RBCCCFEz/hB1Bo0AEQi+edMTIDISMDNDbh7Fxg4EMjPV38bfNJAKOQGadNEQAB3ISAxEXjyRLN1EFLNGOi7AdWWuBi4OhEAU/AkAyAArk4CnPoBQpGCZTSTlJQExhi8vb0VPu/t7Y3Xr1/j+fPnqFOnDkQiESwtLeHg4CC3XGFhITZs2ABPT08AwIQJE7BgwQLJ8wsXLsSqVaswcOBAAIC7uzsSEhKwceNGjBw5UrLcpEmTJMsok5GRgT///BPnz58HAAwfPhzt27fHjz/+CCsrK5X229raGkZGRjAzMyu1LwCwePFiBAQEAABmzJiBDz/8EHl5eTAxMcHKlSsREhKCcePGAQAmT56MixcvYuXKlejEX20FMGzYMLlgPS0tDS1atJAE0or6actKSUmBo6NjqcdPnDiBnJwcyUWJ4cOHIywsDKNGjZI8f/nyZSQmJqLR23IwDw8PuX0XCAQK97s8//d//ye57+bmhilTpmDPnj0IDQ1V+honJyfJ/ri6uqq9TUIIIYToQcn+3CXVqQMcPMgNsHbmDPDDD8C0aeptQ7Y/d4kKSpXZ2QHNm3PTmZ0+zfU7J6SGo0y3pp6fKZ3hlsOAnPvccpWIMe4iQMlS8pLMzMwkATcA1KtXD8+ePQMAPH/+XDJQm4WFheRv0aJFuHv3rtx6FGV2S9q5cyc8PDzw3nvvAQCaN28ODw8P7N69W619K4uvr6/cvgCQ7E9iYiLat28vt3z79u2RmJgo91jJffnqq6+we/duNG/eHKGhoZKLBsrk5ubCxMSk1ONhYWEYMmQIDAy4a1zBwcG4dOmSpAT+2rVrcHZ2lgTc2vTHH3+gQ4cOcHBwgIWFBWbPno20tLQyX8NXUOTIlpARQgghpGqTHblcGR8fYMoU7n6J30EqqcggarKoxJy8Yyjo1lTu4/KXUWc5FTVo0AACgQAJCQkKn//vv/9ga2tbZhk0ABgaGsr9WyAQSAJ2sVgMgCsxv3btmuQvLi6u1Mjb5ip86W7evBnx8fEwMDCQ/MXHx8uVmAuFQsn2eeoMZia7P/wFB34/ZB/jMcZKPVZyX3r27InU1FRMmjQJjx49QpcuXcos7a5duzZev34t99irV6+wb98+/Pzzz5J9d3JyQlFRkWRAuZLdBFQl+57xZI/ZxYsXMXToUPTs2RMHDhzAv//+i1mzZqGAHwBFCX4gPnt7e43aRQghhBA9KC/TzatVi7vNzFR/G9oKuvnB1Ep09SOkpqKgW1Om9bS7nIrs7OzQrVs3/Pzzz8jNzZV77smTJ9ixYweGDBkiCSiNjIxQXKzeaOp169aFk5MT7t27hwYNGsj9ubu7q7WumzdvIjY2FlFRUXIBfExMDK5cuYK4uDgAXIBXciC3a9euyf1bk30BuJL7s2fPyj12/vx5pSX6suzt7RESEoLt27djzZo1+OWXX5Qu26JFi1IXQ3bs2AFnZ2dcv35dbv/XrFmDLVu2oKioCL6+vnjw4AFu8/9ZlqBsv0ses6SkJLns9Llz5+Dq6opZs2bBz88PDRs2VKmfdlxcHAwNDdGkSZNylyWEEEJIFaFq0M137dNn0P3BB1y/87t3ARpDhrwDKOjWlP0HgJkzAGVl3ALAzIVbTsvWrVuH/Px8BAUFISYmBvfv38eRI0fQrVs3ODk5YfHixZJl3dzcEBMTg4cPH+KFGiNVzps3D0uXLsXatWtx+/Zt3Lx5E+Hh4Vi9erVabQ0LC0ObNm3QsWNHNG3aVPLXoUMH+Pv7S7LdnTt3RmxsLLZu3YqkpCTMnTtXEpDL7sulS5eQkpKCFy9eyGWyyzJt2jRERERgw4YNSEpKwurVq7F3795yBySbM2cO9u/fjzt37iA+Ph4HDhwoM1APCgpCfHy8XLY7LCwMgwYNktv3pk2bYvTo0UhPT8fBgwcREBCAjh074qOPPsLx48eRnJyMw4cP48iRI5L9zsrKwsmTJ/HixQtJYN25c2esW7cO//zzD2JjY/Hll1/KZfwbNGiAtLQ07N69G3fv3sUPP/yAyMjIco/XmTNn8MEHH2icgSeEEEJIJcvPlwav5QXdlpbcbUaG+tvRVtBtZQXw3fqoxJy8Ayjo1pRQBLRa+/YfJQPvt/9utUarg6jxGjZsiNjYWHh6emLIkCHw9PTE559/jk6dOuHChQuoxZcNAViwYAFSUlLg6empVrnwmDFjsGnTJkRERKBZs2YICAhARESEWpnugoICbN++HR999JHC5z/66CNs374dBQUFCAoKwuzZsxEaGorWrVsjMzMTI0aMkFt+6tSpEIlE8PHxgb29fbl9k3n9+/fH2rVrsWLFCjRp0gQbN25EeHi4ZDoyZYyMjDBz5kz4+vqiY8eOEIlEZfZDb9asGfz8/CQjk1+9ehXXr19XuP+Wlpbo3r275KLDn3/+idatWyM4OBg+Pj4IDQ2VZLfbtWuHL7/8EkOGDIG9vT2+++47AMCqVavg4uKCjh07YtiwYZg6dSrMzMwk2+jXrx+++eYbTJgwAc2bN8f58+cxe/bsco/Xrl27MHbs2HKXI4QQQkgVce8eIBZzAXXdumUvWxUy3QDQsyd3S9OUkneAgJXsFFrDZGRkwNraGm/evCk1UnZeXh6Sk5Ph7u6ucAAsldzfy41iLjuompkLF3C7lD2qtyyxWIyMjAxYWVlBKKRrIdXVoUOHMHXqVMTFxVXL9/HgwYOYNm0abty4IRn4jVddz1GtfM5JtVFYWIhDhw6hV69epcauIKSqoPO0konFwE8/cYN3NW2q79boxv79QP/+QKtWQGxs2ctevgy0bcvN5a2ktFvpOfrLL8AXXwD9+gH79lWszcnJgIcHNwp6cjJAM6YQNVWF79KyYk1ZNGVYRbkM5KYFe36GGzTNtB5XUq6DDDep+nr16oWkpCQ8fPhQbh7x6iI7Oxvh4eGlAm5CCCGk2jp5Evj6a8DREbhzB6iJ3adUGbmcV1Uy3e7u3IBqp04BW7YAc+ZUfJ2EVFHVJ11VlQlFQN1AwC2Yu6WA+502ceLEahlwA8DgwYPRtm1bfTeDEEII0Z6HD7nbR4+Adev02xZdUXUQNUC+T7e6Ba980C3Tna1CRo/mbsPDuYoEQmooCroJIYQQQkjN9eaN9P7SpUB6ut6aojPqBN18pru4GMjLU2872sx0A8DAgYC1NZCSAkRFaWedhFRBFHQTQgghhJCaSzbIfv0aWLFCb03RmaQk7laVoFs2YFZ3BHNtB92mpkBwMHd/82btrJOQKoiCbkIIIYQQUnPxQTc/iNqaNcDjx/pqjfZlZkr3R5U+3UKhtMRc3X7d2g66AWmJ+Z9/ylclEFKDUNBNCCGEEEJqLj6QGzYMeP99ICcHWLRIv23SJj7LXacOYGOj2ms0nas7J4e71WbQ7efHXRDJywPKmJqVkOqMgm5CCCGEEFJz8ZluGxtg2TLu/i+/cCOZ1wTqjFzOq0qZboFAmu2mEnNSQ1HQTQghhBBCai4+021tDQQEAD17AkVFNWeKKnUGUePxg6npu083b/hwwMCAm0M8Lk676yakCqCgmxBCCCGE1FyymW4AWLKEu921C7h2TQ8N0jJNgu6qlOkGAHt7oE8f7n54uHbXTUgVQEH3OygiIgI2qvb5qeKioqIgEAiQXkWm/3j58iXq1KmDlJQUvWxfIBBg3759AICUlBQIBAJc08MPipCQEPTv31/y70GDBmH16tWV3g5CCCGkVNDdvLl0xOxvv9VDg7RMnZHLeVUt0w1IS8y3bQMKCrS/fkL0iILuaur+/fv47LPP4OjoCCMjI7i6umLixIl4+fKl3HJubm5Ys2aNfhpZwpIlSyASibCM70+lpsDAQEyaNEm7jdKypUuXok+fPnBzcwMgDXz5P1tbW3Ts2BHR0dE6b4uLiwseP36MpvxoreUoGShr05w5c7B48WJkqPufOyGEEFJRsuXlvAULuHLmw4eBSvg/WWcYqxmZbgDo0QNwcACePwcOHtT++gnRIwq6q6F79+7Bz88Pt2/fxq5du3Dnzh1s2LABJ0+ehL+/P169eqWXdhUWFpb5fHh4OEJDQ7G5GgySUd6+KJKbm4uwsDCMGTOm1HMnTpzA48ePER0dDSsrK/Tq1QvJycla27YiIpEIDg4OMDAw0Mr6KsLX1xdubm7YsWOHvptCCCHkXcJY6Uw3ADRoAIwdy92fOZNbrjp68YLbP4EA8PRU/XVVMdNtYACMHMndrwa/FQlRBwXd1dD48eNhZGSEY8eOISAgAPXr10fPnj1x4sQJPHz4ELNmzQLAZYZTU1PxzTffSDKtso4ePQpvb29YWFigR48eeFxizsrw8HB4e3vDxMQEjRs3xs8//yx5js/g/vbbbwgMDISJiQm2b9+utM3R0dHIzc3FggULkJ2djZiYGLnnFWVZJ02ahMDAQMnz0dHRWLt2rWRfZEu4r169Cj8/P5iZmaFdu3a4deuW3LrWr18PT09PGBkZwcvLC9u2bZN7XiAQYMOGDejXrx/Mzc2xaNEivH79Gp988gns7e1hamqKhg0bIryMfkaHDx+GgYEB/P39Sz1nZ2cHBwcH+Pr6YuPGjcjJycGxY8eUbhsA/v77b7Rq1QomJibw8PDA/PnzUVRUJFlnUlISOnbsCBMTE/j4+OD48eNy21RUXh4fH48PP/wQVlZWsLS0xAcffIC7d+9i3rx52LJlC/bv3y85vlFRUQCAhw8fYsiQIbCzs4OHhwf69+8vd+yLi4sxefJk2NjYwM7ODqGhoWAKfrz07dsXu3btUnr8CCGEEK3LzgaKi7n7JbvWzZ4NmJkBFy4Af/9d6U3TCj7L7eICmJqq/rqqmOkGgFGjuNtDh4BHj3SzDUL0gILukhjjvlAq+0/FK6yvXr3C0aNHMW7cOJiW+HJ1cHDAJ598gj179oAxhr1798LZ2RkLFizA48eP5YLqnJwcrFy5Etu2bUNMTAzS0tIwdepUyfO//vorZs2ahcWLFyMxMRFLlizB7NmzsWXLFrltTp8+HV9//TUSExMRFBSktN1hYWEIDg6GoaEhgoODERYWptL+8tauXQt/f3+MHTtWsi8uLi6S52fNmoVVq1YhNjYWBgYGGM33CwIQGRmJiRMnYsqUKYiLi8MXX3yBUaNG4fTp03LbmDt3Lvr164ebN29i9OjRmD17NhISEnD48GEkJiZi/fr1qF27ttI2xsTEwM/Pr9x9MTMzAyCf0S657aNHj2L48OH4+uuvkZCQgI0bNyIiIgKLFy8GAIjFYgwcOBAikQgXL17Ehg0bMH369DK3+/DhQ0mQfurUKVy9ehWjR49GUVERpk6disGDB0suvjx+/Bjt2rVDTk4OOnXqBAsLC0RFReHw4cOSizQFb/tbrVq1Cps3b0ZYWBjOnj2LV69eITIystT227Rpg8uXLyM/P7/cY0QIIYRoBV9aLhJxAbasevUAvtvat99Kg/PqRJPSckCa6VYn6GZMN/N0y/LyAtq3B8Rirm83ITWE/utOq5qcHMDCovK3q2J5T1JSEhhj8Pb2Vvi8t7c3Xr9+jefPn6NOnToQiUSwtLSEg4OD3HKFhYXYsGEDPN+WIk2YMAELFiyQPL9w4UKsWrUKAwcOBAC4u7tLgr+RfOkPuGw0v4zyXcvAn3/+ifPnzwMAhg8fjvbt2+PHH3+EFf+lXw5ra2sYGRnBzMys1L4AwOLFixEQEAAAmDFjBj788EPk5eXBxMQEK1euREhICMaNGwcAmDx5Mi5evIiVK1eiU6dOknUMGzZMLlhPS0tDixYtJIE0309bmZSUFDg6Opa5THZ2NmbOnAmRSCRpr6Jtf/rpp5gxY4bkWHt4eGDhwoUIDQ3F3LlzceLECSQmJiIlJQXOzs4AuD7zPXv2VLrtn376CdbW1ti9ezcMDQ0BAI1k/pM2NTVFfn6+3PHdvn07hEIhNm3aBMYYMjIysHnzZtSqVQtRUVHo3r071qxZg5kzZ+Kjjz4CAGzYsAFHjx4ttX0nJyfk5+fjyZMncHV1LfM4EUIIIVohW1peouIPADBtGrB+PRAfD2zfLi1vri40GUQNkGa61Skvz82VJolKXsDQptGjgXPnuBLz0FDF7xsh1QxlumsYvqy3ZCl5SWZmZpKAGwDq1auHZ8+eAQCeP38uGajNwsJC8rdo0SLcvXtXbj2qZHZ37twJDw8PvPfeewCA5s2bw8PDA7t371Zr38ri6+srty8AJPuTmJiI9u3byy3fvn17JCYmyj1Wcl+++uor7N69G82bN0doaKjkooEyubm5MDExUfhcu3btYGFhAUtLS/z999+IiIhAs2bNlG776tWrWLBggdzx57P8OTk5SExMRP369SUBNwCFZe2yrl27hg8++EAScKvi6tWruHPnDiwtLWFlZQVnZ2fUrl0beXl5uHv3Lt68eYPHjx/LbdvAwEDhecFXZuTwV8kJIYQQXVPUn1uWjQ3Xpxvg5u2ubtVYlZnp5kvLAd0G3R9/zGXSb98GyvntRUh1QZnukszMgKysyt+uiYlKX3wNGjSAQCBAQkKCwpGm//vvP9ja2pZZBg2gVOAlEAgkAbtYLAbAlZi3bdtWbjmRSCT3b3MVyos2b96M+Ph4uQG9xGIxwsLC8PnnnwMAhEJhqX7A6gwoJrs//AUHfj9kH+Mxxko9VnJfevbsidTUVBw8eBAnTpxAly5dMH78eKxcuVJhG2rXro3Xr18rfG7Pnj3w8fGR9HsuqeS2xWIx5s+fr7CKwMTERGGf6fIutJTsjqAKsViMVq1aYceOHRCLxcjKyoKFhQWEQiHs7e3VWhc/wJ+6ryOEEEI0pmjk8pImTADWrgXS0rg5or/8snLapg2aBt2aZLr5oNvEhCvX1xVLS2DwYO692LyZKzcnpJqjTHdJAgF3da2y/1QsnbGzs0O3bt3w888/Izc3V+65J0+eYMeOHRgyZIgkADMyMkKxmn2U6tatCycnJ9y7dw8NGjSQ+3N3d1drXTdv3kRsbCyioqJw7do1yV9MTAyuXLmCuLg4AFwgVnIgt5LzS2uyLwBXcn/27Fm5x86fP6+0RF+Wvb09QkJCsH37dqxZswa//PKL0mVbtGiBhIQEhc+5uLjA09NTYcCtSMuWLXHr1q1Sx79BgwYQCoXw8fFBWloaHskMMnLhwoUy1+nr64szZ84ovZih6Pi2bNkSSUlJqFOnDho0aAAPDw9JO6ytrWFtbY169erh4sWLktcUFRXh6tWrpdYfFxcnyZQTQgghlaK8TDfADUDGj4uyfDkgM2hplSYWa15eXpFMt676c8viu9zt2aOfZBghWkZBdzW0bt065OfnIygoCDExMbh//z6OHDmCbt26wcnJSTLYFsD1Q46JicHDhw/x4sULlbcxb948LF26FGvXrsXt27dx8+ZNhIeHY/Xq1Wq1NSwsDG3atEHHjh3RtGlTyV+HDh3g7+8vGVCtc+fOiI2NxdatW5GUlIS5c+dKAnLZfbl06RJSUlLw4sULuUx2WaZNm4aIiAhs2LABSUlJWL16Nfbu3Ss3cJwic+bMwf79+3Hnzh3Ex8fjwIEDZQbqQUFBiI+PV5rtVsecOXOwdetWzJs3D/Hx8UhMTMSePXvwf//3fwCArl27wsvLCyNGjMD169dx5swZyaj1ykyYMAEZGRkYOnQoYmNjkZSUhG3btklGendzc8ONGzdw69YtvHjxAoWFhfjkk09Qu3Zt9OvXD2fOnEFqaiqio6MxceJEPHjwAAAwceJELFu2DJGRkfjvv/8wbtw4pPM/cmScOXMG3bt3r/CxIYQQQlSmStANAJ99BtjbAykpgBa7v+nUgwdAXh431Za6Y6VokunW9SBqstq3Bxo25AL9P/7Q/fYI0TEKuquhhg0bIjY2Fp6enhgyZAg8PT3x+eefo1OnTrhw4QJq1aolWXbBggVISUmBp6enWmW9Y8aMwaZNmyR9jwMCAhAREaFWprugoADbt2+XDLBV0kcffYTt27ejoKAAQUFBmD17NkJDQ9G6dWtkZmZixIgRcstPnToVIpEIPj4+sLe3R1pamkrt6N+/P9auXYsVK1agSZMm2LhxI8LDwyXTkSljZGSEmTNnwtfXFx07doRIJCqzH3qzZs3g5+eH3377TaV2lSUoKAgHDhzA8ePH0bp1a7z//vtYvXq1ZAAyoVCIyMhI5Ofno02bNhgzZozcxRZF7OzscOrUKWRlZSEgIACtWrXCr7/+KinNHzt2LLy8vODn5wd7e3ucO3cOZmZmiImJQf369TFo0CC0bdsWY8aMQW5urmQQvClTpmDEiBEICQmBv78/LC0tMWDAALlt5+XlITIyEmP5OVEJIYSQyqBKeTnAdS+cPJm7v3Qpl0Wu6vjSck9PLvBWR1XPdAsE0mw3zdlNagABU9Q5tAbJyMiAtbU13rx5U2qk7Ly8PCQnJ8Pd3V3pAFiVRSwWIyMjA1ZWVhAK6VpIdXXo0CFMnToVcXFxNe59rMg5+tNPP2H//v2SuckrU1X6nBPdKywsxKFDh9CrVy+1Bg0kpDLReVqJpk8HvvsO+OYboLxqvTdvuIzxmzfA3r1AiQvIVc769cC4cUCfPsBff6n32ocPAWdnrm92YWGpbo4Kz9GDB4HevQE/P+DKFS3tRBnu3eMuKIhEQEEBUMN+V5GKqwrfpWXFmrLo7CVEi3r16oUvvvgCDx8+1HdTqhRDQ0P8+OOP+m4GIYSQd42q5eUAlw2fMIG7v3ixdHqsqkrTQdQAaXl5cTFXoq6Kysx0A4CLC3dbXAy8HYyVkOqKgm5CtGzixIlw4f+jIACAzz//HF5eXvpuBiGEEN6TJ8C+fVwGsSZTtbycN2kSV2p+9Spw/LjOmqUVFQm6LSyk91Xt113ZQbehIcB3mXz6tHK2SYiOUNBNCCGEEPKuyM4GFi4EGjTgyqe3bNF3i3RLnUw3ANSuDbydzhRLluiiRdpTkaBbKJQG3qr2667soBsA6tblbp88qbxtEqIDFHQTQgghRCExY0jNLEDCq3ykZhZAXNXLbYlyxcVARAQXoM2ZIw2gSswUUuOoG3QDwJQpXJY1Oho4d04Xraq4wkIgOZm737ChZuvg+59W1Uw3IA26KdNNqjk1hzqsmWr4WHKEvNPo802IZm6l5+PEg2xkFkpHcbY0FKKrszm8bIz12DKitpMngalTgWvXuH+7ugJt2gC//y4N3GoqdcvLAW6AsZAQ4NdfuWz3wYM6aVqFJCdzF1LMzABHR83WwffrrsqZbgcH7paCblLNvdOZbn6Uuxx+3kFCSI1T8La/okgk0nNLCKk+bqXnIzI5Uy7gBoDMQjEikzNxKz1fTy0jaklI4Eab7tqVC7itrbmRvP/7TzodU00PujXJdANAaChXgn3oEPDvv9puVcXJlpaXGHlcZZTpJqTSvNOZbpFIBBsbGzx79gwAYGZmBoGmX1wVJBaLUVBQgLy8vBo31RSpGarjOSoWi/H8+XOYmZnBQN05TAl5R4kZw4kH2WUuc+JBNhpaG0Gop/8zSTkY44LG77/nsqEGBsBXX3Fl5bVrc8u4u3O3ycnc8jX1vdQk0w1wfd6HDgV27uTm7f7tN+23rSIq0p+bp2mm28xM822qi4JuUkO8879CHd6WrfCBt74wxpCbmwtTU1O9Bf6ElKW6nqNCoRD169evVm0mRJ/uZxWWynCXlFkoxv2sQrhaGlVSq4hajh8HVq7k7g8YACxbVjo4c3XlbrOzgRcvAHv7ym1jZcjPB3JzufvqZroBYMYMLuj+4w/g1i2gKs3CoY2guzplumkgNVLNvfNBt0AgQL169VCnTh0UFhbqrR2FhYWIiYlBx44d9Ta5OyFlqa7nqJGRUbXJzBNSFWQXqjYOgqrLET1YvZq7nTAB+PFHxcuYmHB9gR894rLdNTHo5rPcgDTAVEezZkDfvsBff3EXLsLDtde2iuKDbk0HUQOqR59uynSTGuKdD7p5IpFIr30+RSIRioqKYGJiUq0CGvLuoHOUkHeDuaFqVSGqLkcqWVwccPQo1x/5m2/KXtbdXRp0t2lTOe2rTHzQbWkJaPob79tvuaB7+3Zg3jxphYC+JSVxt9rIdFfloJsGUiM1BKV/CCGEECLhYmEIS8Oyfx5YGgrhYkEX36qkNWu42wEDAA+Pspfl+3WnpOiyRfqj6SBqstq2Bbp0AYqKpCX7+padDTx4wN3XRp9uVcvL+YGH9ZHpfvYMEJfd7YWQqoyCbkIIIYRICAUCdHUu+0d1V2dzGkStKnr6FNi2jbs/eXL5y7u5cbc1dQRzbQTdAJftBoBNm6pGxvXOHe7Wzg6oVUvz9VSHTHedOtxtURHw+nXlbZcQLaOgmxBCCCFyvGyMMcDdslTG29JQiAHuljRPd1X1889AQQHw/vtAu3blLy87gnlVlZLCtXPaNPVfq+nI5SV16sQd07w8aSWBPmljEDVA/Uy3PoJuIyPA1pa7T4OpkWqMgm5CCCGElOJlY4yvmtgiuIEV+rpaIriBFb5qYksBd1WVm8sF3YBqWW6gegTdy5Zxgfcff6j/Wm1lugUCYNIk7v7hwxVblzZoK+iuDplugAZTIzUCDaRGCCGEEIWEAgFNC1ZdbNvGTf3l6sr151YFH3SnpnL9ZavaTA9PnwIREdz9Fy/Uf722gm6Ay3QDQHw8NxWZsR4vPmlj5HKgemS6AW4wtf/+o6CbVGtV7NuVEEIIIYSoRSwGvv+euz9pEmCgYk7F2Zkb1buggBvFvKr58UcuwAWArCyuvFsd2iovB4D69bky56IiLvDWJ22MXA5UjynDAMp0kxqBgm5CCCGEkOrsyBEuE2hlBYwerfrrDAy4YBKoeiXmWVnATz/JP/b8uXrr0GamWyAAmjfn7l+7VvH1VYS2y8tVyXQXFnJ/AAXdhGiAgm5CCCGEkOps1SruduxYaSClqqrar3vTJi5obtRIGnSpW2KuzaAbAFq04G7//Vc769PEy5fcHwA0aFCxdamT6eaz3ID+gm5NBlIrLgZGjQIGDeK6YNAI6ERPKOgmhBBCCKmurl0DTp3iysS//lr911fFuboLC4HVq7n7U6dKp41SN9OtzfJyoGpkuvnScmfnige/spluxspelg+6DQy4EcUrk4MDd6tJpjs2lhsX4M8/gREjuHOpe3dg/fqq2aWC1FgUdBNCCCGEVFd8X+6PP5aWiqujKs7VvXs3cP8+l+H89FPA3p57XJ/l5YA0033tGtePXh8SE7nbipaWA9JMd3Fx+f3l+aDbzKzi21VXRcrL09K4WwcHoGlTrk/+8ePAuHGAkxPg7w989x1w96722kuIAhR0E0IIIYRUR48eAbt2cfe/+UazdVS18nLGuCAI4AaFMzEBatfm/q3v8vLGjblRy7OygHv3tLNOdWRmAosWcfdbt674+iwspPfL69etr0HUgIoF3ffvc7eBgcDNm1x/+OXLpaPRX7wITJ/OXcSIidFKcwlRhIJuQgghhJDqaN06rhS7QwegTRvN1qHroPvOHWmZtyqOHAHi4riA8Msvucc0zXRru7zcwABo1oy7r49+3V9/zQX7rq7AzJkVX59QKA28y+vXXRWC7mfP1K8w4INuFxfutmFDIDQUuHABePiQm9vex4dbb1WYg53UWBR0E0IIIYRoU3n9Y7UhOxvYsIG7P2WK5uvhg+4HD6SjU2vLiRNcdtjLixtdXRXLl3O3X3whzVDzQbe+M92AfIl5ZfrjD65vskDADQimrQsJqo5grs+gm+/TX1io/kBoJYNuWY6OwFdfcRUVANf/mxAdoaCbEEIIIUQbjh4FvL0BPz+un6wubdnCBSCenkCfPpqvx8GBK+EWi6X9X7UhMxP47DPuODx9CnTuLB0ETJlLl4DoaMDQUBoIAdLycnUy3WKxNHurrQAVkA6mVpmZ7ocPgc8/5+7PmAF88IH21q3qCOb6DLqNjbk50gH1S8zLCrp5fn7cbWxs5VwwI+8kCroJIYQQQiri8WNg6FCgRw8uo/vPP9xjulJcLB1AbdIkbuRyTQkEuhlMLTSUC+Ld3LiS7MePgU6dyh6wasUK7vaTT7jRuXmalJfLjsitzaC7sjPdYjEwciR3gaVVK2DePO2un890lxd05+Rwt/oIugHN+3WrEnQ3bcoF9unpNKAa0RkKugkhhBBCNFFczPWrbtwY2LOH6yPLB8C6nA/4wAGur7StLTcHcUVpe9qwkyelpe9hYVyZuY8Pl7Ht1ElxcJ+UBOzdy92fOlX+OU3Ky/nSchMT7k9bmjXjLlQ8fqzZwF7qWrOGO55mZsCOHdqfrovPdFfl8nJAs6C7oEA6t3dZQbehobSCgUrMiY5Q0E0IIYQQoq5//uFGQP7f/7iApXVr7ge7hwf3vK6CbsakI1h/8YV2giBtZrr5snKA6y/buTPXJ/fkSa5v9/37XOCdmir/ulWruH3r3Rto0kT+OU3Ky7U9iBrPwkI6XZeus93Xr0sHTFu9mjt+2qZqprs6Bt0PH3LnlLGx9MKNMrIl5oTogF6D7nnz5kEgEMj9OTg4SJ5njGHevHlwdHSEqakpAgMDER8fr8cWE0IIIeSdlpEBTJwoDbKtrbkRkC9c4EqP+b6nugq6Dx7ktmtmpvk0YSVpcwTzGTO4gNrVVTooGsD1HT91ihs9OjWVC8b50t8nT7hBwgBu+qaS+IDp5UvV+8rrYhA1XmX0687N5crsCwqAvn2lfbq1rbpluvnMtSr488vZmatOKAsfdF+5on7bCFGB3jPdTZo0wePHjyV/N2/elDz33XffYfXq1Vi3bh2uXLkCBwcHdOvWDZnlXY0jhBBCCNG269e5gdJ++IHraztsGNeH+6uvpGXltWpxt69eaX/7jAFz53L3J0yQjupcUdoKuk+f5i5AAFxZOR/Q8RwduWU8Pbmprzp35rKRP/4I5OcD/v5A+/al12tnx90ypvrFDF0G3ZXRr3vGDCA+ngs2N20qP2jUVHXJdPNJOXUy3XzQXb9++cvy857/84/uB0Ek7yS9B90GBgZwcHCQ/Nm/vZrJGMOaNWswa9YsDBw4EE2bNsWWLVuQk5ODnTt36rnVhBBCCHmnFBdz/acfPQIaNACOHeP62MpU6AHQbab7r7+4oMDCApg2TXvr1UbQnZUlLSv/8kugSxfFyzk5cRlvNzeuX3rnztJAPTRUcXBpaCgNnlUtMddVeTmg+0z30aPchR0ACA8vvzS6IqpbpluToLus/ty8xo256pGsLOD2bfXbR0g59B50JyUlwdHREe7u7hg6dCju3bsHAEhOTsaTJ0/QvXt3ybLGxsYICAjA+fPn9dVcQgghhLyLNm7kgiwbG+DcOaBbN8XL6SroFoulWe7//U/az1kb+KD76VPpKNXqmjmTC9rr1we++67sZevX5zLe9etzAU56OtdfuW9f5a9RdwTzysh0JyVxQZo2PX8OhIRw9ydMAHr21O76S6oOU4YBug+6RSKgZUvuPpWYEx0w0OfG27Zti61bt6JRo0Z4+vQpFi1ahHbt2iE+Ph5P3vbZqMt/yN6qW7cuUksOviEjPz8f+fn5kn9nvL1yV1hYiMLCQh3shXbwbavKbSTvNjpHSXVA5ynRiefPYTBrFgQAiufNg9jWFlByjgmtrCACUPzyJcRKltHkPBVERsLg+nUwS0sUff210u1rxMICBlZWEGRkoDApiRtpXA2CmBgYrFsHACjauBHMxKT89jk5AceOwaBrVwgePEDR1KlgxcVKS3tFdnYQJiWh6MkTMBX2XfjyJfc+WFkpfR80ZmsLA0dHCB49QtE//4D5+2tt1aLx4yF88gSscWMULV6s3fdZAaG5OUQAxOnpKJbZVslzVJSZCSGAYhMT7R9PFQjs7GAAgD19iiIVty9KTeXa7OioUpuFrVpBdPYsii9fhjg4uGINJpWiKvyfr+q29Rp095S5etesWTP4+/vD09MTW7Zswfvvvw8AEJQoM2KMlXpM1tKlSzF//vxSjx87dgxmZmZaarnuHD9+XN9NIKRMdI6S6oDOU6JN7/30E9zS0/HGzQ3RLi5ghw4pXdbz6VM0BfAoPh7/lLEcoMZ5KhYjMDQU1gBu9+iB/y5dUr3xKgqsVQvWGRmI/eMPPOMHlVKBKC8PnSZOhAGAlO7dcT0/Hyhnv2UZL1wI26QkPKldu8zXtRGLUQ9AXFQUUo2Ny11vk2vX0ADAvRcvkKBGe1TVtl49ODx6hIQdO5CsxaqGD//6C0IAZ0eOxKvTp7W2XmVc7t1DSwDPk5NxUcFx4s/RtikpcABw4+5dpOngeJbH9PlzdAfAnjzBoYMHVerjHpCQABsAlx8/xjMV2uwkFMIPwJsTJ3BGD/tINKfP//NzVKwO0mvQXZK5uTmaNWuGpKQk9O/fHwDw5MkT1KtXT7LMs2fPSmW/Zc2cOROTJ0+W/DsjIwMuLi7o3r07rPjBIqqgwsJCHD9+HN26dYOhoaG+m0NIKXSOkuqAzlOibYIrVyA6cQIAYB4RgZ7t2pW9/PPnQHg4nExN4dCrl8Jl1D1PBX/8AYPUVDArK3isWwcPvoRdi0RhYUBKCtrY20OspN2KCCdPhujpUzAXFzjt3AknHf3WEu3fD1y+jGYODmiiQvtE+/YBADxatoSbGvujKuHFi8DVq2haVARvba0/Lw8Gb6s13//sM92UxpcgKCgAfvgB9sbG6CWzHyXPUdH33wMAmrVti6Y6OJ7lys8Hxo6FsKgIvfz9pQMWlsFg9GgAgN+AAdz86uVp0AD4/nvYpqaiV7du3FgCpEqrCv/nZ5Q3HsJbVSrozs/PR2JiIj744AO4u7vDwcEBx48fR4u3fWcKCgoQHR2N5bJTUJRgbGwMYwVXQA0NDavFD7Dq0k7y7qJzlFQHdJ4SrSgu5qYHYwwYMQIGAQHlv+ZtX2thejqE5ZyDKp2nxcWSebkF33wDQ22NWF6SpycAQJSWBpGqn50zZ4C3ZeWCTZtgyI8yrgtv91v06pVq7Xv7Q1hkZ6f6/qijVSsAgPD69XLfZ5W9eMHdCoXcsRRWwtBLby/gCLOyFO6H5Bx9m80zsLbWTzDKD6aXng7DV6+kfbyVycmRzCBg6OGhWpu9vQFrawjevIFhUhLw3nsVbzepFPr8P1/V7ep1ILWpU6ciOjoaycnJuHTpEgYNGoSMjAyMHDkSAoEAkyZNwpIlSxAZGYm4uDiEhITAzMwMw4YN02ezCSGEEKKqnBxg0iQuQKtuwsK4ObGtrMofHIyn7YHUfv8dSEjgRuGeNEk761REkxHMlyzhbseMAWQGvtUJdQdS0+Xo5YB0MLW4OO31u+anmbO1rZyAG5BOGVbVRy8H1BtMjR9EzcJC9XNAKJRcTEFsrPrtI6QMeg26Hzx4gODgYHh5eWHgwIEwMjLCxYsX4erqCgAIDQ3FpEmTMG7cOPj5+eHhw4c4duwYLEvO+0gIIYSQqmnjRmDtWm4k5urk5UtuRG4AWLCg/Mwajy971UbQXVwM8OPUTJmi23JjdYPuggIgJoa7//XXummTLD7o5rPB5dHl6OUAd7wsLbmy5//+0846+aBbhdJprakuo5cDmgXd9eurN8c5P54BjWBOtEyv5eW7d+8u83mBQIB58+Zh3rx5ldMgQgghhGjX3r3c7Y0bXJZSl3MOa9OsWVwQ1LQpMH686q/jM92vXnFl6er84C9p924uoLO15crcdUndoPvyZa6KoXZtoEkT3bWLx0+RVhWmDAO4rGjz5lwFx7VrqvUZLo8+gm7ZTHdZ5ys/WFRVCLrfznBUJnWmC5PVujV3S5luomV6n6ebEEIIITXU06fcnNa86Gj9tUUdsbHAL79w93/6CTBQI0fBB93FxRWbw7moiMuwA8DUqdLgSFfc3Ljb9HRpwFoWfmTtTp0qpxS6qpWXA1zQDXDzt2uDPjPdxcVAXp7y5apCptvBgbtVJ9OtbtDNZ7pv3OCqGFS1YgU3ENujR+ptj7wzKOgmhBBCiG7s389lz3inTumvLaoSi7lSeMaAYcOAjh3Ve72ZmXTQpoqUmO/cCdy+DdjZAf/7n+brUZWFhTSwVSXbzb+XnTrprk2yZMvLZc8pRRjTfaYbkPbrvnZNO+vTR9BtYSG9r6xft1hctTLdugy6XV25z1xhIRd4qyInh7tAdvcuEBWl3vbIO4OCbkIIIYToBl9a7u/P3VaHoDs8HLh0iQtGVqxQ//UCQcX7dctmuadNk2YjdY3PdqeklL1cXh5w4QJ3v3NnXbZIii8vz8uTZl2Vyc3ljiGg26BbNtNd3oUAVegj6BYKpYG3sn7dubnS+9Ul6E5L427VDboFAvVLzPfvl1a1qDhnM3n3UNBNCCGEEO1LT5cG2d9/z/24v3ULePhQr80q06tXwIwZ3P158wBHR83WI9uvWxPbtnFZs9q11etPXlGq9uu+cIErva1XD2jUSPftArhgz8SEu19eiTmf5RaJdBskNmnCVTWkp0uDvIrQR9ANSLsuKAu6ZS9ymJrqvj3KVEamG1B/MLVt26T3KegmSlDQTQghhBDtO3iQK9H08QHatpWW4vJ9gauiOXO48mUfn4qNyF2RacPEYsm83AgNlS//1TVVg27+YkrnzhUbKE4dAoHqI5jzQbe1tW7bZ2QkHUROG/269RV085UUysrL+aDbzKzypjJThO/TXd5AaoxpJ+hWJdP95Alw9Kj03xR0EyUo6CaEEEKI9vGl5QMHcrd8GbKmQXd8vG5/0L55Ix087ccfpf2yNVGRoPvpU+DePS64GTdO8zZoQtWgW3YQtcqk6gjmlTGIGo8vMddGv+6qnunWZ2k5IM10P3tWdjn/mzfScm9Ngm6+vFyV75xdu7gLZTwKuokSFHQTQgghRLtycoAjR7j7AwZwt3zQrUm/7shIbuquKVO00z5Fjh7lMvNeXhXvp1yRoJsPKO3sKj/IUSXozsri+rwDlR90q5vp1mV/bh5fwfEuZLr1HXTXqcPdFhSUPcI+n+W2s+Oy8+pydOS6TojF5b+vfGk5/56VN94AeWdR0E0IIYQQ7Tp2jAu8XV2lQUmHDtzUWykpqs8Fzfv1V+724EGtNlPOX39xt337Vnxd/A9wTfp08wEln9WtTHzQnZKiPJN47hw3SJmrq3T5yqJqprsyg+6akOnmg+6qnuk2MZFWL5TVr7sipeU8VUrM4+O5oNzAgJvpAKBMN1GKgm5CCCGEaFdkJHc7YIC0T62FBde3G1Av2/3iBXD8OHf//n3dzINbVAQcOsTd10bQXZFMtz6D7vr1ufcrJ4cr4VVEtrS8svpz81Sdq7syy8vfe4+7TUsDXr6s2Lr0XV6uSp9ufVNlMDVtBN2qjGDOZ7l79eI+OwAF3UQpCroJIYQQoj2FhdKsMd+fm8eXI6sTdO/dK53+CZCWNmvTuXNcgGxnJ53erCK0UV7OB5iVydhYOmK7smnDZAdRq2xVsbzc2hrw8ODuX7+u+XoKC6VBr51dxdulDg0y3UVFYsTGPkJxsVjxa3RFlcHUtJnpVjaCuVgM7NjB3R8xQnpBgoJuogQF3YQQQgjRnqgoLuipUwdo107+OdnB1FSd13jXLu6W/1F78aI2WimPv0jw4YfcNFMVVV0z3UDZ/brfvAGuXuXuV3Z/bqBqlpcD2unXLXuuVFa7eapmumWC7h9+uITWrX9Fnz67kJtbqOMGylAl063pHN2y+KD71i3FxyUqCnjwgHuvevemoJuUi4JuQgghhGgPX1rer1/pANbfn8umPn7M/Zgtz6NHQHQ0d3/aNO5W20E3Y8D+/dx9bZSWA9W3TzdQdtAdE8Nl+Bo2BJydK7ddQNUsLwek/borEnTz54qNjXYu/KhDg0z3rVvceXr48B306bML2dkFumyhVGWVl9vbc+MWAMA//5R+ni8tHzyY+07jjw0NpEaUoKCbEEIIIdohFgP79nH3S5aWA9xASO3bc/dVKTH//XcuKG7XjvtxC3B9LGXLzSvqv/+Au3e5OZe7d9fOOqtreTlQdtCtr6nCeFWxvByQZrorMpiavvpzAxplut+8yZfcP3kyGT177kBGRn7JV2pfZQXdgPIS85wc4I8/uPuffsrdUqablIOCbkIIIYRox6VLXBbbykp5n191pg7bvZu7HToUaNyYW29ODhAXp532AtLS8s6dpRm/iqqp5eV80K2P/txA1S0v5zPd//0H5OZqtg59Bt3lZbr5QFIm6OYD7M8/bwkrK2OcOZOG7t23IT09T5ctLT/oZowr+wYqHnQrG0xt3z5u6jx3d+lFxBoSdIsZQ2pmARJe5SM1swBiVbsBkXJR0E0IIYQQ7di7l7vt3ZvLHCvCZ0lPn+Yy48okJ3Ol5EIh8PHH3C0/+rk2S8y1OVUYjw+609PL3kdFqmrQ/fKlNJMbGFiZLZLiM93p6dzAY8pUdnm5oyPXtuJizS8IVdNMd1BQA5w6NQK2tia4dOkhunTZipcvdRh4ljeQ2vPnQH4+N7K+k1PFtqVs2jC+tHz4cOkI/jUg6L6Vno/18a+x604G/krNxK47GVgf/xq30iuhguEdQEE3IYQQQiqOMWnQrai0nNe6Nffj/dUr4OZN5cvt2cPdduok/aGt7aD7+XPgwgXufu/e2lknIA26xWLl2cOy2gTov7w8LY0LInl833ofH2m2sbLZ2nIXX4Cyp+eq7Ey3QFDxwdSqcqZbQdDNZ7qtrIzRqpUjoqJCYG9vhn/+eYzAwC14+jRLN20tL9PNl5Y7OCi/8KeqVq2423v3pOfbkyfAsWPcfb60HKj2Qfet9HxEJmcis1D+ImFmoRiRyZkUeGsBBd2EEEIIqbibN7kfpyYmQI8eypczNAQ6duTul1ViLltaznv/fe5WW9OGHTzIXSxo0aLipaiyTE254wCoN5gaY/rPdDs5AQYGXCZZdk50fU4VxhOJpEFpWSXmlR10A9ISc037dVezTDcfdFtbGwMAfH3rIjo6BPXqWSAu7hkCAiLw8KGS9VWEbNCtqPRZW/25Ae78adiQu8+P2r9rF3cxrW1b6XOA9NhUw6BbzBhOPCh7ALgTD7Kp1LyCKOgmhBBCSMXxWe6gILkf5wqV1687MZGb89jAQD5rzme6//tPs/7SJemitJynSb/urCyg4O0o0PoKukUioH597r5sibm+B1HjqTKYWmWXlwPvXKb7zRuu77aVlbHkMW9ve8TEjIKLixVu3XqJgIAIpKama7etfNBdUCB9n2VpM+gGSpeY86XlslluQJrpzs5WfTrEKuJ+VmGpDHdJmYVi3M+qxKnhaiAKugkhhBBScfxUYQMGlL8sH7hFRyseiZzPcgcFyQchtWsDnp7c/cuXNW8rAOTlAUePcverStDNZ29NTcu/cKFLJft1P30KJCRwZdQBAfprF1D+YGoFBdJsoz4y3TduyJflq6qqZLoVBYwlgm7GmFx5uawGDWohJmYU3N1tcPfuawQFbUdxsZrjGpTFxETaXkUl5tqYo1uW7Ajm8fHcRRUDA2DIEPnl+KBbLJZeOKsmsgtVu0ig6nJEMQq6CSGEEFIxd+5wwYZIBPTpU/7yzZtzAVFmprRsk8eYNOgODi79Wm2VmJ86xQVnTk7SLKU28cGTOkG3vkvLeSWDbj7L/d57gJ2dftrEK2+ubtnsJx+cVYaGDbnAKyeH+zyoqypkuouLuYtRJZUIurOzCyWxubW1SanF3dxsEBMzCtbWxrh16yUuXHig3faWNZiatjPdsiOY81nuXr1Kf0b5oBuodiXm5oYCrS5HFKOgmxBCCCEVw2e5O3VSLWgQiaQjYPMBHe/aNeD2bS6jpSgDzQfdFR1M7e+/udu+faUjEGsTn+lWp093VQ+69dmfm1deeTkfdFtYcBnJyiISSY8bH/ipQ59Bt4WF9L6iEvMSQTdfWi4SCWBqqvgYOztboW9fLwDA3r2J2msrUPZgatoOulu04L4fHjwANm3iHitZWg5wY1Xw51s1C7pdLAxhaVh2SGhpKISLhWEltahmoqCbEEIIIRWjTmk5T1m/7l27uNvevRXPmy0bdGvad5IxaX9uVTLzmqhIebm+Ri7nlQy6+fdI3/25gfLLy/UxiBqvvJG1y6LPoFsolAbeigZT44Put9lc2dJyQRkXrAYMaAyAC7qZNvs5V2bQbWEBeHtz91++5M4rZTMdVNPB1IQCAbo6l92dpauzOYS6uDj5DqGgmxBCCCGae/RIOu1W//6qv44Pus+e5ebVBbj+kPxUYbKjlsvy9QWMjblgNilJoybjn3+4dpub6y6Q1CToroqZ7vv3uXJpoRD44AP9tgtQvby8MgdR41XXoBsoezC1UplufuTy0qXlsoKCGsDU1ACpqW9w7ZqSebU1oew4FxdLR9zX5mwEfIk5AAweLJ2ZoCTZwdSqGS8bYwxwtyyV8bY0FGKAuyW8bIyVvJKoioJuQgghhGhu3z7u9v33AUdH1V/n4wPUqQPk5kr7Z1+8yA2EZGnJ9ZtUxMhIOn+upv26+Sx3UJDyH9AVVZ37dLu5cbcPH0oHm/Pz008gW1J55eXVMdNdXCxtt76C7rKmDSsRdCsbRK0kMzND9OjRAICWS8yVHefHj7ljaWAg7fetDfxgaoDi0nJeNZ+r28vGGF81sUVwAyv0dbVEcAMrfNXElgJuLaGgmxBCCCGa4wNY2am9VCEQSLPMfPkyX1revz83grcyFe3Xrcupwnia9OmuKuXldetyx58xYMsW7rGqUFoO1Mzy8jdvpF0l+POmsinLdDOmNOjm5+guy8CBXGl2ZOR/2mknoHwgNb603MmJ62OvLQEB3PeVlxfQvr3y5ap50A1wpeaulkbwqWUMV0sjKinXIgq6CSGEEKK5u3e5Wz4QVgdfYn76NDd12G+/cf9WVlrO4+fr1iToTkvjBmsTCpVn07WhOpeXCwTSbPfZs9xtVRhEDaiZ5eX8hRkLC66SQx+UZboLCqRToJUYSK28TDcA9O7dCAYGQsTHP8etW2XMra4OZcdZ2/25ec2acZ+D48fLHnSxBgTdRHco6CaEEEKI5vigUpOyWD6Qu3ABOHwYePaMW0/XrmW/jg/wb9xQ/wfugQPcrb+/bjPK1TnoBqT9ugFuZOayMnyVSba8XNHgXNUx063l/tzDhv0Jf/8wPHmSpfqLlGW6ZT9fapaXA4CNjQk6d+bOJa1lu5UdZ23P0S2rXbvy10tBNykDBd2EEEII0Qxj0iBHk7JYT0/uh2xhITBlCvfYoEHlZ/tcXIB69bjs+D//qLfNyigtBzTr011VyssB+aC7bVvpyMz6xl+QKCqSn5Obp8+gu04d7lbdoPvlS+5WC3OgP3uWjV274nDx4gP07LlDkpUul7JMN19abmjI/UF2IDXV+voOHMiNYq6ToFv2wouuMt2q4j8j1XAgNaJ7FHQTQgghRDNZWdLSU02CHNl+3fxI5OWVlvOv06Rfd0aGtP+4roPu6jxPNyAfdFeV/twAN/AdP72VosHUFJSX5+YW4rPP9uOvv27ptm18MPjsGTcSv6q0mOn+55/HkvvXrj1Bv367kZdXVP4LlWW6S/TnBtTLdANAv36NIRAAly8/xIMHCgZqUxd/nPPz5S+86Dvo1nOmW8wYUjMLkPAqH6mZBRBrc5o2UmEUdBNCCCFEM3wW18io7IHPyiLbV9jBAejYUbXXadKv+9gxLqvesCE3KJIu8UH3mzfSCxNlKSqSHk8KustW1mBqCjLdR47cwebN1xAc/Cfu31eQHdcWPtMt+16qQgdBd5s2TrCyMkZ0dCqCg/9EUVE5FwGUZbr5AFJB0F3elGE8BwcLtGvHBcL79mkh221qKm2vbFXBOxx030rPx/r419h1JwN/pWZi150MrI9/jVvp+ZXeFqIYBd2EEEII0YxsgKPpKLeyAd3gwaqPOsxnutWZNky2tFzXo/LKltsrKoMu6dUraamsFsqMK8zTk7s1MeH6v1clZQ2mpiDofvqUy9bm5BRiypRjumuXsbF0u+qUmGsx6L56lQu6Bw/2wV9/DYWxsQj79v2HL788AFZW5lNJplugINPNl5ermukGpKOYa23qMEX9ut/RoPtWej4ikzORWSh/YSWzUIzI5EwKvKsICroJIYQQohk+m1eRaY7q1weaN+dGEy9rDtyS/Py41zx4wP2Vp6gIOHSIu9+nj0ZNVYuRkTRQUSXryZdK29py8wzrm68vsHgxEBGhu7nMNVXWXN0KystfvJAGQb//noATJ+7prm2aDKamg0x3y5b1EBDght27B0EoFCAs7F98++1J5S8sr093BcrLAWDAAK5fd3R0qtz7obGSxzk/X3q/fv2Kr18Tegi6xYzhxIOy+5CfeJBNpeZVAAXdhBBCCNGMtgatOnCAy1j7+an+GnNzbiofQLVs9/nz3IBVtraVNxK3Ov26q1J/boCrBPj2W2DIEH23pDQ1y8v5IM/UlLuY8b//HUZBgQol/5rQY9D98mUOUlLSAQAtWtQDAPTv3xi//NIbALBs2TmsXn1B8Ys16NOt6kBqAODubovmzR0gFjP8/bcW+taXPM4PH3K3Jib6qxTRw0Bq97MKS2W4S8osFON+VmEltYgoQ0E3IYQQQjSjjUw3ADg5qRdw81QtMS8qAmbM4O737Vt5mWR1pg2rSiOXV3Vqlpe/fJkLAJg82R/29mb4778XWLtWgzneVaHHoPvff58AADw9bWFjI61O+Oyzlli2rAsAYMqUY9i69XrpF6uR6VZnnm5ZfLZ7714t9Ot2cOBun3D7LDddmK67jiijh0x3dqFqGWxVlyO6Q0E3IYQQQjSjraBbU6qOYL5iBTcXuJUVsGCB7tvFUyformqZ7qpMWXm5WCwNGhWUlzdoUAvLl3NzwM+fH42HD7UwknZJegy6r159BABo1cqx1HOhoe0xeTL3eRk9ej8OHLgtv4CyPt18AMkHlNCsvByQ9us+fvwuMjMr2M+45HHWd39uQC9Bt7mhahcYVF2O6A4F3YQQQgjRjD7nRAakQXdsLDcquSLXrwNz53L3f/ihcvt7qjNXNwXdqlNWXp6ZKR2MTkF5ee3aZhg5sjnef98Z2dmFmDr1uPbbJjttmKq0FHT/8w+X9W3Z0qHUcwKBACtWdMenn/qiuJjh449/lwTpANTMdKs3ejmvSRN7NGxYC/n5xTh8+I5ary2Fgm4AgIuFISwNyw7nLA2FcLEwrKQWEWUo6CZECZrvkBBCyqHvTHejRlxGMzcXuHmz9PP5+dzgbIWFQL9+wIgRlds+dfp0V1J5uVjMcPXqIyxeHIMvvzygnUGtKpuy8nL+IpCxsdzgb7JBt1AowE8/9YJAAOzeHYeoqBTttk2PmW5+EDVFmW4AbwdU64tu3TyQl1eEzZv/lT6pYp/u4mIxsrIKAKif6RYIBDIl5hUcxZyCbgCAUCBAV2fzMpfp6mwOob5K7okEBd2EKEDzHRJCiAr0nekWCqXzdSvq1z1vHheM29sDv/xS+X09q0h5+fPn2dix4wY+/TQSDg4r4ef3K/7v/05j48ar+OWXq1rfns4pKy9XMHI5txgXBNnZcXPJt2xZD19+yY0hMGHCIRQWanFQNXWDbrFYK0H3mzd5uHOHW0+LFqUz3TxDQxF6924EAHjxIlf6hGzQLZtkKBF08wE3oN5Aajy+xPzgwSTk5RWp/XqJqhh062EgNQDwsjHGAHfLUhlvS0MhBrhbwstG/feJaB8F3YSUQPMdEkKIivSd6QaU9+s+fx747jvu/saNQJ06ldsuQK9Bd0LCc8yefQqtW/+KunVXYvjwSGzffgPPn+fAwsIIDRpwAV5iooJpt6o6ZeXlCi4C5ecXSQLF2rWl/ZIXLeoMOztTxMc/x7p1l7XXNnWD7sxMLvAGKvQ54gdRc3OzgZ2dWZnL2tpyVQCvXskE3Xx5eVERkJcnfZzP2r4NKPnSciMjEYyN1R+QsHVrJzg6WiIrqwAnT1Zg6jbZgdQYqxpBd0Uz3adPcwM+KusqUwYvG2N81cQWwQ2s0NfVEsENrPBVE1sKuKsQCroJkUHzHRJCiBqqQtDNZ7plg+6sLK6UXCzmbgcM0E/b1OnTrcXy8vj4Z2jZciMWLTqD2NhHYAxo3twBM2a0R1TUSLx8GSoZzfrWrWoYdPPHKCtLPkDkM90KRi4XiQRyfZBr1TLFsmXcoGpz50bh8eMSZdWakg26VfmtwGe5TU25Pw3x/bNbtqxX7rK1anHbef1aJui2sJDelykxF5QIujUdRI0nFEpLzCMjKzCKOX+c8/O5fuh80K2vObqBigfdM2YAy5cDxzUba0AoEMDV0gg+tYzhamlEJeVVDAXdhMig+Q4JIUQN+i4vB6RB9+3b0gAmNBS4exdwdgbWrtVf2/SQ6WaM4X//O4z8/GL4+TkiIqIfHj2ajH///QJLl3ZFQIAbjIxE8PLitnPr1kuw6nYh2dpaOu2bbIk5fz4qGLnczo7rzy1r9OgWaNPGCZmZBQgNPaGdtpUMBstTCYOolWRrywfdMhcshEJp4C3b7hLl5fx0YZqUlvP4EvP9+2+hqKjs31xKmZpKS+Lv3ZN+xqpzpps/F5KTtdMeUqVQ0E2IDJrvkBBSYYwBYWEaZyuqlaqQ6bazAxo25O5fvgwcPQqsX8/9OzxcvxcE1BlITUtB9++/J+D06RSYmBjg998/xsiRzVGvnmWp5Ro0qAWBgMtcPn1auX1QK0wgUFxiruAikOwgaiUJhQKsW9cTAgGwffsNnDmTWvG2yQaDqpSYV8J0YSUpLC8HFA+mpuVMNwB07OiKWrVM8eJFDs6eTdN4PZILHLGxeNsoaZm8PlQ06OaPO5+1JzUKBd2EyKD5DgkhFRYRAYwZAwwaBBRrcYCmqqgqZLoBab/uw4eB0aO5+xMmAF276q9NgOqZ7pwc6Q/1CpSXZ2UVYPLkowCAmTM7wM3NRumyJiYGkuerdYm5bKZbwUBqL1/KD6JWUuvWThgzpiUAYPz4Q5pnXmWp069bC0F3ZmY+bt9+CUC98vI3b/IgFsskERRNG1Yi080H3epOFybLwECIvn29AACRkRUYxZw/zleucLf6zHID8gOpaVI9kpXF3aZV4EIEqbIo6CZEBs13SAipkPv3gUmTuPsZGcB/FeizWNUVFEgDRX1mugFpifkPPwCPHnFTiS1frt82AaoH3XzgaGgozTZqYPHiGDx8mAl3dxtMm9au3OUbN5aWmFc7Wsh085Ys6YJatUxx8+Yz+Wm0NFXJQfe1a0/AGODsbIU6dcqePgqQlpczJi0XB6A4012qvLzimW4Acv26Ne7ewA+mxme69R1085nu4mL1B0MTi6XHmjLdNRIF3YTIqOz5DmkucEJqEMa4DLdsloj/MVgFVfj7hw9wAP2WdALSTDfA9U3dskX6A1if+EAqM7PsH+GypeUa/v9y+/ZLrFp1AQCwdm0PmJqWf3HYy8sOQDXPdGsh6K5d2wxz5wYA4AZVk50WSyOaBN12dhpvjp+fu2XLeip9ro2MRDAzM3y7eQUjmMt8hwmUZLorGnR36+YBc3ND3L+fgatXH2u2Ev4437zJ3VaVoBtQv8RcdpoxCrprJAq6CSmhsuY7pLnACalhfv0VOHYMMDEBevfmHruqxTmQGQNSUoCtW4GxYwEvLy64iIxUe1Wy3z9/30vX7PuHz95aWwMikdpt0CpfX+nIzzNnygfh+iRbdi97kaIkPujWsLScMYavvz6MwkIxevVqKJmHuTyyg6lVOyqWl6sSdAPAl1/6wcPDFk+eZGH16gsVa5s6QffLt8e+ApluPmh1bWKv8u8K6Qjm5WS6lfTprshAagBgamqIXr24sRgiIq5pthL+OBe9ne9b30G3oaH0u1DdoJsvLQeAhw9rftekdxAF3YQooOv5DmkucEJqmJQUYMoU7v6SJcDQodz9imS6GQMSErg5pj/5hJsKx90dGDkS2LSJG637zRvg44+5QFxF/PdPwavXGDTxE0zs1Ag2affU//5RYxC1vLwi3Y6QbWjIDZ42eTIwZ47utlOO4mIxYmJSER//jHvAwEAayJRVYs5nazUcRG3//ls4evQujIxEWLu2BwQqZsulme5qGHSrXF6e+3bxsoNuIyMRli7lplH77rtzePIkq8zly8TPCV9J5eV8pvtNXWuVf1fwg6nJTRumQp9uvhy9opluAPjii1YAgM2b/8Xz5xoM5scH3Tx9B90CgeaDqcle6Cgq4uYfJzUKBd2EKKGr+Q5pLnBCahixGPjsMy5T0aED8PXXgJ8f99y1a9IsjDrevOEy2U2aAF9+CezcCTx4wAVx778PTJsG/P03MGoUlxEZORL48cfym/r2+8c29S5GjAhCgzPHYJKVAacb0osDKn//qDiI2pUrD2Fru1wywJfOjBwJrFoFGBnpdjslMMZw4cJ9fP31YTg5rUZAQATatduM7Oy3Jcqq9OuuwMjlOTmFmDTpCABg2rR2aNBA9eCNz3QnJ79GQUE1y6ypWF5e3kBqsj7+2AetWzsiO7sQ8+dHad62SuzTnZ1dgMRE7vxx8lZeKVHyc83365YrLy+rT/fbYDIjgzuvtRF0d+7sDj8/R+TmFuHHHy+rv4KSQbc+5+jmaRp0Z5W4yEMl5jUOBd2EVDKaC5yQGmbDBuDUKa68OTycKy9s2JD7AZubCyRqMDrvyZNAUhIXQHbqBMydyz2Wng5cuAB89x1Xwr5pk3Tgtq+/BhYtKnPU3PtZhbCLOYmRI4Jgl3pX8rjpG+mUVip//6iY6f7++4vIyytCRMR17YwMXQUwxnDjxlPMnHkCHh4/oF27zfjxx8uSqbcyMvLx+PHbH9F8MKVK0K1Befny5WeRmvoGLi5WmDmzg1qvrVfPAhYWRiguZrh7V4VpzaoSLZeXA4BAIMCKFd0AAL/++g/++0/Dvu6VGHTfuPEUYjGDZW0zWNkrH5Om5OdaYXl5yUx3cTEEeW+f1+I83TyBQIDp09sDANatu6x+X3qHEnOS6zvTDciPYK4O2QsdAAXdNRAF3YRUMpoLnJAa5O5dLusMcKNlN2jA3RcKgRYtuPua9Os+e5a7/ewzLqCfNw/o3Fn6g44nFAKrV3PPA8Ds2Vx7FAXejMF43Y8Y/L+hMMl8gwe+rZHYrR8AwDRdPuBS6ftHhUz3y5c5+PPPxLeL5+HixQflr7cKy8kpxJIlZ9C06Xq8994GLFt2Dikp6bCwMMLw4b44eHAYXFy4wEVSLqvKXN0alpffu/cay5efAwB8/30QzM3Vy/ILBILqW2KuxdHLZQUEuKFPn0YoLmaYOfOkZm2rxKCbLy13bFz+BRvZz7XC8vISmW6DApkgWMsDqfEGDGiMhg1r4fXrPPz6q5rflSUz3c7OWmlThWgr003ThtU4FHQTUsloLnBCagixmCvvzskBAgOB8ePln+dLzDXp132OC6TQvn35ywoEXCb8+++5f69axQ20JjsQT0EB8PnncPh2KoRiMW70GYpdv0TihQc3V65ZiaBbpe8fFTLd27ffkCtbPnLkTvnrraLEYoahQ//ArFmnkJDwHEZGIgwY0Bi//TYIT59OxbZtA9CrV0PUrWsBQBrs6bK8fNKkI8jPL0bXrh4YONBb3V0CIDuYWjUbwbxkppsxrQTdALBsWVcIhQLs2/cfzp7VIPipxKCbH0StrNJynuznmg+6yxq9XMRnuQUCyUCF2pinW5ZIJJRMb7d69UX1ujnIBt21a0sHU9QnKi8nSlDQTUglo7nACakhfvwROHOGywBt3sxlnWW14gYJUjvTnZMD/PMPd7+DGuXCkyZJ2xEWBgQHc8H28+dA167Apk1gQiHOTV2AQ/N+QLGRMXJtuB/6punSLKfK3z/lBN2MMWzaxM157O/PZaAOH66+QfeCBdH4++/bMDYW4ddf++DZs6nYu3cIPv64iWT6JUAa3GkUdKtRXn7w4G38/fdtGBgI8cMPqg+eVlK1zXTzx+rlS+4CWF6edFq2t+XleXlFyM7mHlMn6Pbx+X/2zjvMiXLtw3fa9souS116B6kqICJIU0FUVASxc/TYRbF9tmM5do+IFetRjwWQoqAggqhURZHe+7IL23vf7Ga+P95Mym6STbLJJru893VxbUgmM28mk5l53ud5fr+W3HqrqFR5+OE1nosAqsFgWVndYMoWRfFZprtr3ySXy9X+XbujXm4JuiMiLFZ2vvLptuWGGwbQunUUaWlFzJ+/2/03RkRAlJjkCorScvCNkBrIoLsZIoNuiaSRaWwvcIlE4gcOHRLWVAD/+Y9QFTej+uQe6dYfAMVTMbU//xTLt2vnuTDQLbfAN98INe9Fi2DiRDjnHDE5EBOD5ocfSHziEcsNdJk56LbNdLt9/qmnvPzPP0+xZ08WYWF6Pv1UlLFv25ZOZmYDVKEDxHffHeDZZ9cB8OGHk7n11sFOM30tW4qb7uxs8023Oz3dHpaXV1RUM2uWEE974IFh9HYjy+kMNej2un/Zz2RklDB+/Be8/fYW+xdUX2uTSQSu6vGo1VoCMVVETa/XehwkPvPMaCIiDPzxRxpLl3qoyxAVZc26usp2l5ZaJwq8CLorKqrZu1ccOzPGdnK5bO3ftSqk5iro1leaFc9t2lp8XV4OEBam54EHhM3fK69swmTyYJJD7etu6kG3Ojmj7msH5eXueLBLghcZdEskAaCxvMAlEokfqKkRwW15ucgg33675SVb/+vFuiQqI6PQVFRw4o8d7q/ftrTcm8m3q64Syubh4UJ8LSUFunaFP/6ASy6xO/+Ux4nAJbwgz/PzTz2Z7o8/Ftn6qVP70LNnIoMHtwHgp5+OOlw+WNm/P5sbbhBe6Pfddy433jjA5fJOM92uero9LC9/5ZWNHD2aT9u20Tz11AVuvccZwe7Vfd99P/Lzz8eYO7dW0G0wWCd8cnKsQXdsrKXqRP0OEhLCPa4EaNMmmoceGg7A//3fWoxGD8qeNRr3SszVYyIkxBqsecDu3ZlUV5tITIxgVN9Ej+4r3LEM07kIun0hpGbL7bcPISYmlP37c/jhh0Puv1Hdz8ESdDdUSK1PH/G3Vqbb9tpSnwe7JDiRQbdEEiD87QUukUj8xLvvwubNIiv08ceWwFj1v7a4E2i1ZPQWAdrenze7f3Okiqh5Ulpem4sugjVrhLDQpZeK7Hlva8+vev4Z1V9k0lsU53t+/nGR6S4urmT+/D0A3HrrYAAuvrgr0LT6ugsKKrj88gWUlFQxalRH/vOfCfW+x+PycpNJlEiDW+XlO3Zk8PzzGwCYM2cC0dENu2Z07y4yrHl55dYxBwnff3+QRYv2AZCeXly3zNtWTK2ByuWOeOih80hKiuTIkTw+/NBLka+sLOfL2JaWezHBpvZzDxnSRojieXBf4Y5lmK6WcrnRWENZmcjM+zLTDaJH/M47hQ7Gyy9vdL+kv42YzAsKuzBoeKZbPU9nZoJ50qPOtcWMMw92SXAig26JJID4ywtcIpH4icpKePll8fiVV6BjR8Dqf10bNehuvW+He/7XJpOwBAP3RNRcMWKEKFH8/nuHpatajYa2HcUNqy43x/MbAheZ7m++2UtpqZEePRIYOVLcDF9ySXdAZLpraoLfOsxkUrj++qUcPpxHhw6xLFo0FYNBV+/76pSX1xd0FxRYRe/UkmknVFZWc+ON31JdbeLKK3tzzTV93fkoLomMDLEorgeTmFpxcSV3373S8v/y8mpLP7EFW69uH4mo2RIdHcozz4wC4Nln11myvG7hSaa7gf3cahUJuH9f4Y5lWO3y8uLiKptFfZ8gmDVrKKGhOn7/Pc19AbsHHoDp0+G663w+Hq9oaE93p04QZm5dSUtzem2xxa1riyTgyKBbIpFIJBJ3+eILSE8XGeR//MPydGqJsU4WAiCjz0AAWu/f5Z7/9d69ImMXGQn9+zd8vPVN5KlBXk2NNVPoLi4y3aqA2q23DrKU9Q4b1p7Y2FDy8srZuvW0Z9sKAE8//SsrVhwmLEzPt99Oo6ULD2Rb6mS66+vpVkvLo6Mh1HUg89xz69i9O4uWLSOYN2+S1+JptQnGEvMnn/yF1NQiOneOIzpaWKGlp9cSm7JVMFePXx8G3SAqNXr0SCA7u4zXXtvk/hsbIei2zXR7Sr2WYYpSJ9OtenSHh+vdmoDylDZtornpJjFRqVrh1ct558H8+UIDIxhoaKY7OtqatU9NdXptscWta4sk4MigWyKRSCQSd6ipgddeE48feED0YZpx5mud0VsEzq0O7UFrNNbvf62Wlg8fDnp9g4dcL2FhVvXfHA+znE4y3Xv2ZPHHH2no9Vq7/me9Xsv48aLE3FMV8507M+jUaS7vvfeXZ2P0kqVL91tKuD/6aLJdJrE+PO7pdlO5fMuWNF5+WQQi779/KUlJ7k0CuINVwdy3me60tCImTPiC2bN/8qi64c8/T/H2238C4rO2by8ysOnptUT4bMvLbXu6zeTmioAyIcF7KymDQcfLL48F4PXXf+f06eJ63mHGz0F3VVUNu3eLdXtyfKqo5eXFxVXWfnU1011dDRUVdXq6/SGiVpuHHx6BVqthxYrDls/XpGho0B0VZe1PT02t/5phxt3lJIFDBt0SiUQikbjD8uVCtTwuTvhg2+DM1zq/fWcqoqLRV1WSePxg/f7Xnvhz+wo1cPEk6DaZrEFOraBbFVC77LKeFs9qlUsu6QZ4HnQ/99x6UlIKefdd/wfde/dmcdNN3wFw//1Duf56zyoO1Iy42z3dbiiXl5cbuemm7zCZFGbMOMtrT25n+MM2LDu7lPHjv2DNmmO88cYf3HCDKIuvD6Oxhttu+x5Fgeuv78+ECV1p00ZkYOsEvH4uL1e54openHdeMuXl1bzwwnr33uTnoHvPniyMRhPx8WF06hTn8fvj4qzq+wUF5ox2lM3vtbgYfa1Mt689uh3RrVsLrrpKHN+vvrrZb9vxGw0VUrMNuk+erP+aoW7WzeUkgUMG3RKJRCKR1IeiiB5ugLvuspZhmkmOMtRRDQZAqyWzl8j2djq0u37/66YSdJvLTwG7IKeiopovvtgFYPE5tuWii0Sm+6+/Trkt2pWSUsB33x0AYN++bLKzPbyZ9YD8/HKuuGIhJSVVjBnTmddeq184rTZqgFdQUCEyiGrQXVYmfNNr44Zy+ZNP/sLBg7m0aRPF229f4vGY6sPX5eUFBRVcdNGXHDiQQ6tWkej1WubP38P11y+tN/CeM+d3du3KJCEhnDlzxP5v21b83twqL/ehkJqKRqPhiSdGArBypZsTRn4Oum37ub1pM7C1UbP0ddvYrVFU5KC83P+ZboBHHxXnv/nzd5OSUuDXbfkcH5eXO7222FDbg10SnMigWyKRSCSS+li/HrZsET23991X52WtRsO49o7LfVUxtUEn9rgWSzx1Ck6cEDe+w4b5YtTu4U3QrWZtw8Ksoj8IP+u8vHKSk2OYMKFrnbe1axdD//6tUBRYvdo967D33vvLzrd3wwY3BZa84JZblnHkSB4dO8aycOHV6PWe3ybFx4dZWulzc8tFEKg+4SjbXU95+YYNKbzxxh8AfPzxZRYBLF+iZrqPHs1zKxvtitLSKiZN+prt2zNISopk/fpbWLx4KgaDloUL9zJjxhKn9ltHj+bxzDPCD33OnIssVQNt2ohA0K3ycj9kugFGjuyAVqvhxIkCTp0qqv8NXgTd7vgwq8v8vCkNgEGDWnv0OWxR+7qdKZhbhNTMgWRjlJcDDBnSlnHjulBTo/D667/7dVs+p6FCarXKy11dW1Rqe7C7Qnp9Bw4ZdEskEolEUh9qlvuWW6w307Ww9b+2paC/yPjG79nhehtqlnvAgDqZdL/iTdDtRERNLS2fOXMQOp3jWwxPrMPKyowWUTbV2mrduhPuj9MD9u3LZtmyg+h0Gr79dprXgZpOpyUhwaavW6ezZl8dBd0uystLSqq4+eZlKArMnDmQiRO7ezWm+khOjiU8XI/RaOL4cSdl8G5QUVHNFVcsZPPmVOLiwliz5gZ69Ejg8st7sWTJNRgMWhYt2se119YNvBVF4Y47VlBRUc3YsZ254QZrWb/ToNs20+3gmFR7un0RdEdHhzJggPjtb9qUWs/SeBx0u+PDbLvMpq2nAMhtFeO1ZZRVwbyuV7emuNipkJqvPbodoWa7P/54W9BZ2bnEF5lum6AbnF9bnHmwO0N6fQcWGXRLJBKJROKKXbvgxx9FBvqhh1wu6sgnd/zlF4gXd+4EowuF2UCUlkPDMt02/dzHjuWzdu1xNBq45ZaBTt+qWoetWnXELoPtiK+/3k1eXjmdOsXx7LOjAVi/3j+Z7k8/FcH9pEk9GDTIc2EqW9Qgz1IK70pMzUV5+aOPruHYsXw6dIjljTcubtCYXKHVaujevWF93UZjDdOnL+bnn48RGWngxx+vo39/6wTV5Mk9+fbbaYSE6FiyZD/Tpi2mqsoaeH/55S5+/vkYYWF63n//UruSabW83GVPt4vycnUSpKGMGCGCoU2b3DgG1aC7qAgqKhwvYz4eTofF1OvDbOvVXGOsIeOQ+J5adE/02qtZFVOzsw2zyXQHQkhNZezYzgwZ0oby8mreeedPv2/PZ/gi062Wl5+0HmeeeLA7Qnp9Bx4ZdEskEolE4gpVsfzqq6Fr3ZLp2tTxye3WTQQClZXCEswZqnL5+ef7YNAe4KOg+5NPRJZ7woSudOwY5/St552XTFRUCNnZZWzfnu50OUVReOutLQDcc885XHhhZ0Aomdtl5nyA0Vhj6UV3NWHgLk4VzD0oL//552O8995WAP7738v8Hug0RMHcZFK45ZZlLFt2kNBQHcuXX8uwYe3rLDdpUg+++24aoaE6vv32ANdcs4iqqhpycsp44IGfAHj66VF062bf46wKqdXp6bYtL1f3rZ/KywFGjBDBkFuZ7thYq8OBs2x3rgic/6p23TKwJrXEzqs563g+1VU1hEaF0KK9mGTwxqvZYXm5jVe3cyE1/wfdGo3Gku1+//2tft+ez/BWSM1Rpruw0BqM474He22k13dwIINuiUQikUickZIiPGABHnnEu3VoNDBkiHi81cnNY3Ex7NghHjeFTHetUt7qahOffroDEL7GrggJ0TFuXBfAtYr5unUp7N6dRUSEgZkzB9G6dRQ9eiSgKG4GPR6watURMjNLSUqKZNKkhpdwe+TV7aC8vLCwgpkzlwFw111nM3ZslwaPqT68VTBXFIW7717BV1/tRq/XsmjRVMaM6ex0+Usu6c6yZdMJC9OzbNlBrrrqG+6770dyc8vp378VDz44vM576i0vr6iA02bvd/MxWVZmpKxMVJb4LugWwdCOHRmUlDgQxbNFo6m/xNyc6c6LjHX8upmSasUuQ3n6gDhm2vZMRKsVgZc3Xs0Oy8vNmW5NSUmdTHdjCampqO0UmZmlltL2oMebTHdVlVVkMSpK/FMnj1Ibfq6TXt/BgQy6JRKJRCJxxpw5wp977Fhr4OwNZ58t/v79t+PXt2wRNlwdOkD7uhlCv+KDTPePPx4mPb2ExMQILrusZ71vV63DXPV1qz7NN9zQ31IGO2pUR8D3fd3qhMH115+FwaBr8PpatlTLyz3IdNsE3bNn/0RqahFdu8bzyivjGzwed+jVy3MFc0VR+L//+5n33/8bjQa++GIKkyfX//1fdFE3vv/+WsLC9PzwwyHmz9+DRiM80R3tfzXTXVJSRXGxTRlsZKQQNwRrKa65vDw3V+x7g0FLdHSI25/JFcnJsXToEEtNjcKWLWn1vyEpSfytJ+iuiI13/LoTTu0XQXe73vbVEZ56NauZbrvycjcy3Y0VdEdGhlg81k+eLGyUbTYYb4LuEpvJJFU93sY2rKFIr+/gQAbdEolEIpE4IjcXPv5YPH700YatSw3YnQXdaj93Y5eWAySIDGdDMt2q2NlNNw0gJKT+oPXii0XQ/fvvaQ5LxW1twu6991zL8xdcIIJuX/Z1Z2eX8v33hwC45Za6Nmfe4LS83FVPtzlrW1hYYZkE+PTTy4mK8k3AWB9W2zD3j4N587ZavJQ//HAy06f3c/u948Z1YcWKGYSH6wHxPZ97bjuHy0ZFhVgCZ7tst0ZjzXbXsrBTRdQSEiK8stRyhrWvu4FiauXlll7v8hjPgm5LpruXfdDtqVezOpnlTL3cWU+3P326a9Ohg5hEOSOC7tBQMJitv2xswxqK9PoODmTQLZFIJBKJI955R9w4DRoE48Y1bF1q0L1zp2Ov5kCJqIE1w5rrQVmxTab79OliVqwQQWt9peUqHTrE0qdPS0wmhTVrjtV5XbUJGzu2M337JlmeVzPdf/992j7j2QC+/HIX1dUmzj67Lf36JdX/Bjdwu6e7slIIbYk3AXDqVDGKIrKQI0d29Ml43KFHDzH54m4pr6JY7ZxeeGGM29+9LWPGdGbjxpm8/voEXn7Z9W/MaV93bas1c9Dt635uFZ8F3eYJGEWnIzQuxuVqovQai3K1qcbEafPESLve1uPVG69ma3m5e0JqjV1eDmdI0G0roqZSS8G8IUiv7+DA46B7zJgxFKgz3DYUFRUxZswYrwfy0ksvodFouP/++y3PKYrCM888Q9u2bQkPD2f06NHsdSVCI5FIJBKJLygthbffFo8fecTqs+wtXbqIYKCqqq6YWnU1/G72og1k0J2XJ0rp3cFGtOrzz3dQU6Nw/vkdLCXK7uDMOszWJsw2yw2ivLdTpzhqahR+/92N8t56UBTFklX2hYCailpeXm9PtzrRodNZgkU1qFSDzMYiJibU0jvtTon5jh0ZHDuWT3i4nlmzhnq93cGD2zB79nDCw13f8Ktjq61grtRSfTeZg0b/Bd0iA/n776nU1NTjae5G0K1p0YJxyVF1X7dhfHKUxas5O6UAY0U1IeF6EjtYe8E98WpWsZaXu2cZ1tjl5QAdOzbRoNtodO1WYYutiJqKD8vLfe31LfEOj4Pu3377jSoHs/QVFRVs2LDBq0H89ddffPjhh/Tv39/u+VdffZU5c+bwzjvv8Ndff9G6dWvGjx9PcXGxkzVJJBKJROIDPv1UBESdOwvV8obiSkxt925x0xUTA/3cL8/1GWp5uclkLRuvD3W5+HiWLhVl4DNnDvRos7bWYYqNaq6tTdill/ao8z5f9nVv25bO7t1ZhIbquPZa3+17q2VYPZlutbQ8IUFY0mEtn1aDzMbEkxLzxYv3AULsKjLS/yXwqm2YbXn5wYJKDhusgWdVeATzDpVwsKDSb0H3WWclER0dQnFxFbt3Z7le2I2gmxYt3PJhVpdJ+UNkPtv0bIlWp/XYq9mW+srL9XUy3Y3n062iZrpTUppI0B1pE9y6m+12lOn2YXk5+M7rW+I9encX3LVrl+Xxvn37yMjIsPy/pqaGVatW0a6d414cV5SUlHDdddfx0Ucf8fzzz1ueVxSFuXPn8sQTT3DllVcC8Pnnn9OqVSu+/vprbr/9do+3JZFIJBJJvVRXw+uvi8cPPQR6ty+Vrjn7bFi7VvR133ab9Xm1tHz4cJHxbGwMBiE+VVgogkA1CHeFTXl5evpxAM46q5WLN9Rl5MgOREQYSE8vYdeuTAYMaF3HJkynq5sbuOCCjnz++U6f9HWrWe4pU3pbAhBf4G5Pt8aBiFpGhggqW7cORNCdwG+/nag3060oCosWiaD76qv7NMbQbBTMRYCi+g6PjbXai1VEx1p8h9PSRNl+YqLvvlcAnU7L8OHJrF59lE2bTjJwYGvnC7sZdIMIirrHhpBaYqTUqBBp0JAcZbDLPm5cspdlc8T5YsqlPbi2W0ydZTzBYXm5KqQWJJnuJldeHhIiJtBMJhF02/jGO8VVpttHQTe4d4xJ/Ifbme6BAwcyaNAgNBoNY8aMYeDAgZZ/Q4YM4fnnn+df//qXxwO4++67mTRpEuNq9csdP36cjIwMJkyYYHkuNDSUUaNGsXnzZo+3I5FIJBKJW3zzDZw4IXpFb7nFd+t1lulW/bkDUVqu4qmCuTnTrcTGWgSrPM0ohobqLdZSqnXY+vX2NmGOUDPdf/55ivJy7y1uKiqq+frr3YBvS8sBWrYUQUpOTpnI4teX6bYJuq3l5YEJuqH+8vI9e7I4fDiP0FCdTyzW3MHa011i5ztcHm8NuiujrL3Ru0+K/ZiQ4NtMN3jQ1+1B0A2ufZjnzv2DW2/9HpNJ4bbbBvOfZy7wyKvZEQ7Ly9XAr6ioTqZbCqm5gUbjeV+3GnQ76+n2oX+2t17fkobj9vT98ePHURSFLl268Oeff9LSRrgiJCSEpKQkdB7O0C9YsIBt27bx119/1XlNzaS3amU/c96qVStSUlKcrrOyspLKSqu4SpFZoMRoNGJ0t7ciAKhjC+YxSs5s5DEq8Tvp6aLnuaP34lENPk4zM9G/8AIaoOauuzDp9e735dXHgAEYAGXXLqpLSixWR/pNm9AA1UOHogTo96VLSEB79CjVGRlujUGfn48GKDGEU1FRDUBMjN7j/T5+fCd++OEQP/54mAcfHMrcuX8AcN11/YiKcry+5OQo2raN4vTpEjZtSrEE4Z6ydOk+8vMraN8+mgsuaO/Tc1tsrOhPrqiopqCgjKjoaPHd5+dTbXM/YsoS5cmmhARqzM+dOiXuW1q2jGj0823XrnEAHDiQ7XLbCxaIyYoJE7oSFqZtlHEmJYlA5tSpIk4UlFFaWYUWKLex26qMikFrEsdjYa4IyuPjQ30+vqFD2wCwadNJ1+tu0UJ875mZVNdaTpudjQ4wxcVZvntHKIrCCy9s5LnnRAvn7NlDeemlMZhMNZhMbmowOCEqSoQB5eXVFBeXExamRxMRIYKDnBw0JtGzbgwJobKknMpKsb3w8Mb5zgHathUB/6lTxZSVVfjE0s/f6CMi0JSUYCwsdOv6oS0oEMdCRIT1WEhKQq/RoKmowJieXlcwUAIEx72pu9t2O+juaL4JMpnqEY1wk9TUVGbNmsXq1asJC3M+Y1bb5kFRFJfWDy+99BLPPvtsnedXr15NRITvZzt9zZo1awI9BInEJfIYlfiD6JQURj72GCgKP8+bR5VZVMpbPD1ONUYjXVasoOc336ApK8MYHs6abt0wrlzZoHHYoShcEhVFSEkJmz74gMJu3QjPzmZCWhomrZZV+fnU+HJ7HjDUZKI1sPvXXznpRjn9pXl56IAVm7YBoNdrWL/+Z4+tmUJDxST5pk0nee+9JSxffhCAs84qY6WLfdG1q57Tp+GTT9ZSWuqivNcF//nPUQCGD4/gp59WebUOZyiKgsGgwWhUWLRoBZ0oYAJgys21+1zHtmyhF5BSVsYu8/P79omy+YyMI6xc6YGivA9ITxffx6FDOfzwwwq0Wsff5//+tx+Arl0rXH5PvuSkOXN95EgGu9f/jOoGHm+yluzrDSZ6pon2BDW7nJZ2iJUrPbDDc4Py8hq0Wjh5sojPP/+Oli0d97SHFBVxCaDJz+fHZctQDFaxuN5bt9IDOF5YyB4n+1AI/Z1m+XJhETZjRmtGjqzgxx9/9MnnMJkUSyX04sUraNHCQPyhQ1wAmNLSLOWwP65bR0GJNdu6YcNadLrGyZCaTAp6vYbqaoWvvvqepKTGsdBrCOOASOD3tWvJd6M8vNvWrfQF0oqK2G5zLFwUF0dYfj6bFiygsGtXv423ORDIe9MyNysavGpUO3ToEL/99htZWVl1gnB3S8z//vtvsrKyGKKW2yF6w9evX88777zDwYPiwpuRkUGbNm0sy2RlZdXJftvy2GOPMXv2bMv/i4qKSE5OZsKECcTEuLZkCCRGo5E1a9Ywfvx4DAYp2S8JPuQxKvEb6eno77sPjfnCNSErC9OMGV6typvjVPPjj+gefxzN4cMAKIMGoXn3XcaffbZXY1AUhe++O0jbttEMHWqvdaIbNgx+/pnzIyJQJk5EM3++eGHQIC4y65cEAt2SJbB1K/3btqXfxImuF66oQGcWVO1+7mhgMYmJEUyaNMmrbb/+eiaHD+fx0Ud5mEwwZkwn7rjjKpfvSUvbxoYNq8jMDGVifeN1+P4iduzYAcCzz06hW7cWrt/gBa1aHSMtrZj+/YcxpFsE3H47uqoqJl54IUa9njVr1tDVPLnUYdAg2ps/x6OPfgCUcMklIxg9upPPx+WKmhoTs2Ydoqqqhn79RtCpU1ydZfbtyyYtbQchITqeeOLqRis17tIlh6eeOkpxMZx1wTgWHxNBeGlXE6rGfV5iBw62F0rq2eWpQAljxw7noot8H7C89lo227ZlEBbWnYkT+zpeyGRCueUWNDU1XHL22WCjfaT94QcAOg0eTAcHx3BNjYm7715lCbhff31cHTV/XxAXd4i8vHIGDz6PPn1aCvHIRx5Bb+7nVkJCuGTyZI4ezQf2EBUVwuTJ3v3WvaVjx5McPZpPt26DOf/8Do26bW/QJyZCVhbnDRiAcuGF9S6v/fNPANr17Ekbm2NB17UrbN3K+R06oHhxnjsTCIZ7U7Wquj48Dro/+ugj7rzzThITE2ndurXdrLZGo3E76B47diy7d++2e+6WW26hV69ePProo3Tp0oXWrVuzZs0aBg0SfV1VVVWsW7eOV155xel6Q0NDCQ2tK/BgMBiaRKDQVMYpOXORx6jEp5SWwpVXCluUsDAR0H32GbqHHmqQTZdbx+nBgzB7NqiZhaQkeOklNDffjF7rsbmHhbfe2sKsWato0SKcrKyH7MXAzj4bfv4Z/Y4dQsBsi8jKac8/H20gf1dJwu9Xl5+Prr5xqH3IWi351eJ6m5AQ4fV54ZJLunH48J/s3SuCi1mzhtW7rjFjugDwxx+nUBQtISGelZzOn78PRRGibL17eyYA5y6JiZGkpRVTUFCFoUV7i7iSoaTEUiqqM/f16lq3tuz3zExRFt2+fVyjn2sNBujWrQX79mVz9Ggh3bvXLWldtkxMTk2Y0JXExMazNevYUUyMFBZWkhRiIDI0hGKjidIW1u+vIiYOk1bc2paZPaVbt47xy348//wObNuWwZYtp7n++oHOF0xKgvR0DHl50KmT9XmzLoKuZcs6vzmjsYabb17GwoV70Wo1fPTRZKcaBw0lPj6MvLxyiourxX5qUWsCKjISg8FAWZkoLY+JCW3047JDh1iOHs3n9OnSpnH/Ye6B11dViR9VfZSLnnpdbKz9sdCxI2zdij493b31nMEE8t7U3e16fFfx/PPP88ILL5CRkcGOHTvYvn275d+2bdvcXk90dDT9+vWz+xcZGUlCQgL9+vWzeHa/+OKLfPvtt+zZs4ebb76ZiIgIZniZAZFIfIlJUUgprmJfXiUpxVWYfCh0IZGcEdTUwLXXCjXvxESh4h0WBvv2gQOtD59RWAgPPijsuVauFDczDz0Ehw7BzJkW6yZvWLHiEA888BMgbHj2769V1lpbTE1VLj//fK+36RM8EVJT7cJiY8k1qx43xJZJtQ4D6NQpzi1hrl69EmnZMoLy8mq2bj3t0fb85c1dG6ttWKk4phyJqdUSUisvN1JQIPZpY/t0q1jF1BwfC6pV2NVX9260MQFER4cQEWGemMgosfgOl8Vb1fYro6z7rKJI7MeEBN+ql6uoft1ei6k5EFIDcQxMmbKQhQv3YjBoWbDgKr8F3GLzqoK5WUytdlVoHRG1xreW6tgxDqhrGxa092GeCqk5sgwDvyiYSwKHx5nu/Px8pk6d6o+x1OGRRx6hvLycu+66i/z8fIYOHcrq1auJjg7MhUgiUTlYUMnPaaUUG63tFdEGLePaR0qvQ4nEXWbPhu+/F4Jiy5fD4MFw1VXw1Vfw3//Cub4vpWT7drj4YjALWDFpEsyZAz3q+kF7yq5dmUyfvgSTSUGn01BTo/DHH2n065dkXUgtWd+9G7KzQbXjDKRyOXgWdNvYhanK5Q1RiB41qiNhYXoqKqqd2oTVRqPRcMEFHVmyZD/r1p3gvPOS3d7epk2pHDmSR2Skwa92Vw5tw3Jz7YLu2pZhql1YWJg+IMENuFYwP3gwh927s9DrtVx2Wc86r/sTjUZDmzZRHD2aT3p6Ced3bcGUzrBWq6BoNGgUhYroWKINWka0COGxciGo5mufbhVVwXznzkyKiyuJjnbyfXkYdN988zJWrDhMWJiepUuvsZuU8geqVZ7FNqx24GcOIFWP7sa0C1Pp0EFMBNgqmAf1fZi36uW14xsZdDcrPJ7Onzp1KqtXr/bHWPjtt9+YO3eu5f8ajYZnnnmG9PR0KioqWLduHf369fPLtiUSd1G9QW1P9IDFG/RgQaWTd0okEgtvvglvvSUef/GF8KgGkWkGmD/f/RsWT3j5ZRFw9+ghstw//OCTgDsjo4RLL/2akpIqxozpzP33DwNgy5Y0+wU7dhQ32UYjfPihsILp3BlstEsCgjeZ7rg4S0DZkGxieLiBf//7Qi6/vCe33Tak/jeYueACIfDqqV/3f/+7HYBrrulLVJT/RJlatnTDqzs3V10YsPfo9lSUzlf07CmOBUdBt5rlHjeui099zd1Fzf6fPi0ygz3jQrmjfyKmFmKioH+XVtzZN54W1aIUOiRE57fvuF27GDp1isNkEpNrTvEg6BZ6EAcA+O67aX4PuMGBbZhWax94B9CjW6W2bVjQ34f5wjIMrEH3Sc/OcZLgxONMd7du3Xjqqaf4448/OOuss+rUsd93330+G5xEEmzYeoM64+e0UrrHSu9DicQpy5bBAw+Ix6+8ArbVU6NHi77HEydg6VK4/nrfbVdRYIOw3eHDD2HUKJ+strzcyOWXLyA1tYgePRJYvHgq69al8Prrv/PHH6fsF9ZoRLZ79Wp47z3xXKBLy6EBme6GB90ADz10Hg89dJ5H71GtwjZuPEl1tQm9vv48QklJFd98sxfwb2k52JaX1wq61f2nKKLaQSwMCA9qCIxHt4qr8vLFi4VqeWOXlqu0bat6dRdbntNqNNAyEXJzSGiVABqNZaIjMTHCr5MXI0Ykc+JEAZs2pTJ+vBOxNg+C7oKCCqqqxITBqFGdfDxax6jl5Xl5tby6zYGgEhmJhsB4dKvYBt3e3IcdOJCDVquhR48El+/zGeaJCkpdj9OCs/LyDmbROJnpbhZ4nOn+8MMPiYqKYt26dbzzzju88cYbln+2WWqJpDmSWmKsM7Nam2KjidQS6WUtkThk61aYMUMEHLffDg8/bP+6Vgu33CIe//e/vt32sWPCC9xg8FnpusmkcNNN3/Hnn6do0SKcFStmEB8fblEt37s3i+LiWlkXta/7tLkXOdCl5eBd0B0X55Pycm/p1y+JuLgwSkqq2LEjw633LF68j9JSI926tfC7CnKd8nI1uDLvP315ORrV39USdIub70D1c4M1033qVDElJVWW548cyWPHjgx0Og2XX94rIGNTJyPUyQkLw4aBTgcDBwLYBd3+RC0xd9nX7SjorqqyZjcTrIGgKqIXGxtKWJhXBkMeY810V1iftO3rNgeQhYVqprvxLbtse7pPFld5dB+WllbEwIHv07PnO4wY8V8++2wHpaVVLt/fYHxdXn76NFRX+2ZskoDhcdB9/Phxp/+OHTvmjzFKJEFDqdE9kQ53l5NIzihSUuDSS8WNyMUXwzvvOFYov+km8fyvv4pA2VeoWe5zzoFw35TG/utfv7Jo0T4MBi3ffjvNYj3Vpk00HTrEoijw11+1hL5qW5G5EXSbTApFRZWkphayZ08WmzadZOXKw2zenIriC/EgNeguKBCl765Qy8vterobv9RYp9MycqQInNetO+HWe2wF1Pxdvu20vNwcdIeoNjMREZabdGt5eaRfx+aKFi3CLcHqoUPWEvMlS0Rp+Zgxnf0ezDpDDbrV8nILn3wi2kZ6ickAX1Vg1IcqpvbHH2lUVzsJBB0F3WqWW6OB2FjL0+r336pV41U61OnpBvvgz3xsBrK8PDlZTAKUlFSRnlNRz9IC9T5s69bTVFaK6oHNm1O55ZZltGnzOnfc8QN//+2ZCKPb+EpIrVUr0OuF6Gh6uu/GJwkI3ku0SiRnIJEG927S3F1OIjljKCwUomWZmdC/PyxcKG4mHNGxI4wbJx5/9pnvxrB+vfh7wQU+Wd3//reTF14QgfxHH0229BirDBvWHqBuv+cQm77luDjoU1fMq6Kimssum0/HjnOJi3sZvf45YmNfpkOHuZx11jzOP/9TJk36mhEj/stdd63AZGpg4B0fb50Ase05doSD8vJABWGe9HUfPZrH+vUpaLUabrxxgL+HVm95eagadKsTHtiWlwdWMNZRibm1tNx/4nP1YS0vr5Xp1mrtyrQbK9Pdt29LYmNDKSmpYteuTMcLuQq64+Pt3BIyM609/Y2F0/JyFUumWwS7gSgvDw83WCaxCjLc80RW78MOHBDH8MSJ3XnxxTF06RJPcXEVH3zwN2ef/RGDB3/Ae+/9ZXEN8Am+ynTrdNBeXEdkiXnTx+Oge+bMmS7/SSTNmeQoA9EG1z+baIOW5CjppyiR2DFnDuzdC23bwooVdW1paqNeTz77TMzy+wI10z1ypA9WlcKtty4H4LHHzuemmwbWWWbYMFFivmVLrb7uDh2sJaXnnefQomzVqiN8//0hTp4spLCwEjWZbTBoSUyMoGvXeAYNao1GA++//ze33/59wwJvnc4atNRXYu5QSC0wQbfa171hQ4rLz19WZuTGG78DYPz4LrRvX8/x5wMcqpeDJeAKcRl0B66nG+oqmJ84UcDWrafRajVccUVgSsvBOhlh29PtiMYKunU6LcOHqyXmTiZ+kszuBY6C7lrK5Wp5eatWjVfpUEdIDezOz4pFSK3K/FJglMHVEvPK7FKP7sPUoHv48PY89thIDh++l7Vrb+Taa/sREqJj+/YM7r57Je3bz2HbNh9lk32V6QapYN6M8Djozs/Pt/uXlZXFL7/8wtKlSylQL8QSSTNFq9FYvEGdMa59pBRRk0hsURShRg7w6qvWmXtXXHGFyAKnpsLatQ0fQ0YGHDmCotFwoPeQBnm6Hj2ax5QpCzEaTVx1VW+ef36Mw+WGDrVmuu1KwDUaa0+5ExG1NWuOAnDddWdx4MDdpKc/SFnZ41RWPkl29sMcOXIf27bdzhdfTEGr1fDxx9uZOXMZNTXWElePPWzd7et2aBnW+OXlAIMGtSEqKoT8/Ar27MlyuIzRWMPUqYvYvDmVuLgwXn99QqOMrWVLca3IyysX34uz8nKzcjnYq5cHktoK5qpq+ahRHUlKClzpu9Oe7lo0VtAN1r7ujZtSHf/e1Ex3To61L9dJ0G0tL2/MoLue8vIg8OkGq5haWmqRR/dh6jGsTiRptRrGjOnM119fxenTs3nzzYvp3DmO0lIjCxbs8c1gHQipOT0fK4rzTDdIBfNmhMcqDd9++22d50wmE3fddRddunTxyaAkkmCmZ1woUzoTvP6QEkmwsWMHHD4MYWFw2WXuvScsDK67Dt59VwiqTWhYoHRq1S+0A7K69+G7Ah0UFHn1m1UUhVtv/Z7c3HLOPrst//ufCHodMWhQawwGLVlZpaSkFNKpU5z1xVdegd694Z57HL53zRrRyz51ah9LAOSI667rj16v5brrlvL55zuprjbx2WdXcLTEWO85Kju7lGefXcfOnZksWXINSYmJcPCg1cbKGeagsToqhqIiEegGKtOt12sZMSKZn346yrp1J+jfv5Xd6yaTwsyZy1m58jDh4Xp++OFa+vZNcrI236JORJhMCgUFFSTUElJznOkOvJAa1C0vV4PuQJaWg3W/5OWVU1FR7VRsTJ0Masyge/WvJxh4uNCiFWD5vSUmiok2RRGBd+vWLjLdQVJebluJFAQ+3WDv1e3ufZiiKJZMd69edc+jCQkR3HffUBITI7juuqX8/LOPNERqZbpdeoqH1GApZXKU6ZYK5s0Gn/R0a7VaHnjgAd544w1frE4iCXp6xoVyZ994ru0Ww2Udo7m2Wwx39o2XAbdE4oiFC8XfSZMcz+Q7Qy0x//bb+vuMXXCwoJKMVb8AkDp4uOV5bzxdf/jhEL/9doLQUB2LFk0lIsJ5K0l4uIEBA1oDDvq6zzoLXn/d4f44caKAw4fz0Ok0jB7dqd4xTZvWj4ULr0av1/LVV7u5/JrFLD5c6NTDdndWGW+88Tvdu7/Nu+/+xcaNJ1m79pi15N3N8vJivbix1GisJaqBwFlft6IoPPTQar78chc6nYZFi6ZahK8aA4NBZ8kKZmeX1dvTXVNjspQXB0um+9ChXFJSCtiy5RQaDUyZErjSchDHWWioDrBmhR3hC/94d4nrkYhWr6Ugq5QCm7J3y/mlpMY6saKWmNdbXt6YQmrW8nJLRU6QCalBXa9ud+7DsrJKKSioQKOB7t2d24WNHdsZgB07MqztIA3BJuiuz1P8SFpO3ffZIsvLmw0+E1I7evQo1VLOXnIGodVo6BgdQp8WoXSMlr7cEolDFMUadE+b5tl7Bw2CAQOEvc7XX3u1edXTtf32PwBIHTSszjI/p5W6VWpuNNbw8MNrALj//mH2mWsnqH3ddYJuF6il5UOHtndbtOiqq/qwaNFUDAYtK7/dz4LHVlNjtO+FVxSF/etPMG7oR8yevZrCwkpLlj4zs9Tj8vICjQho4uPD0ekCp8uq9nWvX59iV8b/yiubeOMN8b1/+unlTJrUo9HHppaY5+SUOe/pNpeX5+SUYTIpaDQEtIQboGvXePR6LaWlRt5++08Azj+/Q8Az8BqNxq2+7sYqLzcpCpvzjbQ1T1Kk7KxrXfdzWilKbTG1ICwvNxpNlJWZnQscWIYF0qcb7G3DVOq7D1NLyzt1inNpwdaqVRT9+iWhKPDrr8cbPlhz8KyUldXrKf770WzxICrKob6HLC9vPnh8lZw9e7bdvwceeIDp06czbdo0pnl6QyWRSCSS5s2ff8KJE+LGbdIkz96r0Viz3V56dqeWGKnKyyfpsCiPTXMQdNt6urrio4+2cfBgLomJETz2mONe7NqoCuZ1xNRcoJaWjx/vWcvWFVf04v0vr0Rn0LJn7VG+/r+fqDYH3pnH8vj0nh/43/0ryDpRQGLLCD7+eDJ33SXsy7KyPAi6zZnu3Brh1xuofm6Vs89uS1iYnqysUstN9scfb+Oxx4QWwJw5E7jhBv+rlTvCTkzNNtOtKHXKy9U+5aSkSPT6wJrLGAw6unQR433//a2AaHUIBpzahtnQWEF3aomRYqOJjgPaAJCyo64QV7HRREWiuW/fzUx3Y1Y6REYaMJiFySwl5jaZbqWOT3dwZLrdwVVpeW3GjRPZbp+UmJuDbmNJab2e4sZC83nAUWk5yEx3M8Ljs/r27dvt/u3atQuA119/nblz5/p6fBKJRCJpyqhZ7ssuc1w6Vx/XXQchIbB9u/jnIaVGhXY7/0SjKOQld6Y0sZXT5VxRWFjB00//BsAzz4xyO9ujiqlt25ZOZWX91WA1NSbWrhWZlgkTurq1DVuGjevGDXMmog/Rse/X43z10CqWv7qet6Yt4PDvJ9EZtFxw0yBW/Hk7//jHYEvWMDOzxL2gu6ZG2L8BmVVq0B2Yfm6V0FA9w4eL/bx+fQrffXeA22//AYD/+78RPPDAcFdv9ytW27BSa4BlNEJZmYOgWwSRgS4tV1H7uktLxYTUlVf2DuRwLDi1DTOjKEqjBd3qeaPjQNFGkrLTsfp1VWItBXMHQbeiKJae7sYsL9doNHXF1Gr1dCuKEjRCaunpxVRVuedo4VnQLSY51fNvgzBPVCilrrPcACFl5mWctV6pPd3Z2VBe7ngZSZPAYyG1X3/91R/jkEgkEklzw2SCb74Rj72thEpIEErm33wDn34qSs49INKgIXnb74DjLLftcq54+eWN5OSU0bNnAv/85xCXy9rStWs8CQnh5OaWs3NnJuee287l8tu3Z5CXV05MTGi9yzoi0qCh54iO3PDGRL6YvZIDG05YXutzYWcm3j+ChORYWiWISQO1jDkrqwzOdyPoVgNFILNS9LMHOtMNoq/7119PMG/eVvbvzzYLqA3kxRfHBnRcqrdwTk6ZuBHX64WCdX6+tafbXF6ulhYHuoRbpWfPBL7/Xjw+77xk2rXzv82aO1gVzB1nusvKjFRWiqDM30G3et5QM90Zh3OpKK4kLNo+MNW4UV6en1+B0ZwVbez2gvj4MLKySq22YbXUy8vLq6muFmMLVKa7ZcsIwsL0VFRUc+pUEZ07x9f7ntrK5a644IKO6PVajh7N5/jxfLfW7xTzBLPOjSA5pMw8eeQs0x0fL9ZXVgZpadC9u/fjkgQUr+uXsrOz2bhxI5s2bSI7O9uXY5JIJBJJc2DzZjh1SmRNLr7Y+/WoJeZffgkVFa6XrUVylIFOO7YA9iJqtth6ujri5MlCS2/wq6+Ox2DQub19jUZjZx1WH2o/94UXdvKqxDg5ykC0QUuP4R24+c1LCYsOpXX3BP4x7zJueH0iCcmxdp9X7R11O9Ot2oVFRJBdKDKggc50g7Wve8eODCora7jiil588MFki5J0oLArLxeKc+KFvDyn5eWB9uhWsVXNv/rq4Mhyg3VS4vRpx5luNcsdGqpzKXToC9TfW0zLSFq0j0FR4OTuTLtlog1aotqLoNxV0K1muWNjQ132H/uDOgrmtXq61Sy3RgORkSGNOjYVjUZjyXbb9nW7wpNMd3R0KEOHionOBme7LUF3Wb2e4jGVDiY6bNFoZIl5M8HjK3ppaSkzZ86kTZs2XHDBBYwcOZK2bdvyj3/8gzJ3TeAlEolE0vxZsED8veIKCG1AdmTcOOHtnZ8Py5d79FZtRQWt94qydEciamDv6eqIxx9fS2VlDaNHd2LyZM/FuDwRU1u92rt+bhWtRmPxsO16bnue/PkWZi2cTrehyZZlbD+vNdPtZk+3GnTHxQXco9uWoUPbW/pSR43qyPz5VwW8Lxpsy8vN90fmoFuTk0OI6s0b5OXlIIT6ggVrebnjTLdtabm/J11sf2+Wvu5aJebj2keibS3Kz5WsLFKKq6jKERlYU7w1mxpI5fo65eW1Mt22yuXOLBIbA0/6uisqqjl+XJyv3Am6wYcl5uagW1NWVq+n+IAws56Is0w3SNuwZoJXQmrr1q3j+++/p6CggIKCApYtW8a6det48MEH/TFGiUQikTQ1ampg8WLxePr0hq1Lp4ObbxaPPRVU+/NPNEYj1a3bUNPZPpCNNmiZ0jnapdXf1q2n+eqr3QD85z/jvbqJd1dMrbS0ik2bhELt+PGe93OrCA/baKINWnQ2WXlHn1ftHc3MLEVxxzLMLKJGfDy5uY3TN+sOEREGXnxxLNde249ly6Y3eqbQGXaZbrBmNo+JyRVFo7E8l5Ehgq5gyXSfe247xo7tzF13nW0JdoIBa3m560x3Yx2X6u+t5+C2gFVMze73Zi4vz0k5zfwjRZhyRaZ7fq7OYlloVS4PRNBttQ0D7DLdSkREwD26VWy9uuvjyJE8FEVUDrhbrq9ah61dewyTqX5HC6eo+iVVVfSM0lnOx7aox0ebGvNEh6ugW2a6mwUeX5WWLFnC4sWLGT16tOW5iRMnEh4ezjXXXMO8efN8OT6JRCKRNEXWrROllC1aiEx1Q7n5Znj+eVi9Wtx4JCfX+xYA1q8HQH/BSO7s14LUEiOlRkX0ekcZXGa4FUXhwQdXA3DDDf0ZMqStV0M/5xyR6T52LJ/s7FKLjVTdoaZgNJro2DGW7t1bOFzGXXrGhdI9NqTez6v2HFdUVFMSFkM0QHExVFY6rk5QM93x8eTkBE+mG+Chh84L9BDqYNfTDdZM9+HD1v/rxa2YmrkNlp7u0FA9P/98Y6CHUYf6LMMaO+gG8Xt7/JqeLPj3r5zem8XVHSPpEh9m+b2diIinExCWk4WmupqwEtFakBMZy7fHi5nSGRsRtca3i1ODbkfq5URGUpQdWOVyFWt5eUG9y9qWlrs7WTp0aHsiIw1kZ5exe3cmAwa09m6gtqKhZWX0jItxfj5WK16clZeDtA1rJnic6S4rK6NVq7rqr0lJSbK8XCKRSCQCVbX8yivBUH9fZUlJFddfv5Rp0xZbBHvs6NoVRo8Wvt+ffOL+ODZsEH8vuKBeT9faLF9+kPXrUwgL0/PCC2Pc32Yt4uLC6N1blDe6ynbbWoX5oizWnc8bGRlCZKT4fjIr9KKqACA31/FK1Ux3XJwl0x0MPd3BSp1Mtxp0Hzok/p9gLeFWM7fBUl4erKiZ7uzsMocq1mrbQ2NXYPTtk0RcXBjlZUZKUwstvzeTovBbtRhzZH4O4UX5lvdURMcBwstbzXQH4vtXe7odqpdHRlrswgLl0a2ienWfPFnkekE86+dWCQnRMWpUJ6CBJeZhYaIXG4QAGi7Ox8XmySNZXt7s8TjoHj58OE8//TQVNmI25eXlPPvsswwfHjhbDolEIpEECUYjLFkiHruhWp6bW8a4cf/jq6928803e/nppyOOF7z9dvH3nXfADSsWqqvhd6FczsiRbgzcitFYwyOP/AzA7NnDSE5uWHmtO2Jq1qDb+9Jyb1DLWbNyyq1BoLMSc5tMdzD1dAcrTnu6j4hjXDErlyuKYqNeLoNuVyQkRFj699XssC3qBEdjH5darYZu3USFyokTBZbnU0uMZEWL35W2pob4VBHMVURFo5irHIqNJo6dEsFXYDLddXu6TRMnkjVgALRoYdfTHUg86en2RLncFrXEvEF+3RqNNdtdX0LSk0y3DLqbNB4H3W+++SabN2+mffv2jB07lnHjxpGcnMzmzZt58803/TFGiUQikTQl1q4VmdKWLUV22gVpaUWMHPmpXQb4o4+2OV746qtFxjs3Fz76qP5x7Nghbmji4qBfP7eHD/DBB39z6FAuLVtG8Oij53v0XkeoYmrOMt3p6cXs2ZOFRmO96Wss1H5HtxTM7YTUZKa7PtRWgpKSKioqquv0dKuTHMXFVZSVCUElmel2jVarsewjR33dgSgvV0lOFhnitDRrJrbUqGAyGCiPFRMuCcdFlUNFrH0LidrTH8iebkt5uUZDzXff8fuzz4JGE3CPbhXboFtRXPdce5PpBquY2rp1KW77gTvE3aDbnUx3Uywvf+wxcc2urg70SIIGj4Pufv36cfjwYV566SUGDhxI//79efnllzl8+DB9+/b1xxglEolE0pRQS8unTrX0qzri0KFcRoz4L/v359CuXTSLFk0F4IcfDjnu19Tr4dFHxeP//Ef0HbvC3M/NiBGgdf9yV1hYwTPP/AbAs8+O9kl2xyqmlkZNTd3yeTXLPXhwm0YPYtXMmlsK5ubyciUuznKDHgxCasFKbGwoOp0oI83NLbNmuo1mxWLz/laz3NHRIQGzZGpKWG3D6p4ngiHoTk21Bt2ql3dpC1HVkHBc9POXx8TZvTc/J3Dq5dbycse+0sEipNa+vdi/ZWVGS6WNIxRF8Tro7tcviaSkSMrKjG45TjjFH5nu4mIodM8uLaBUVMCrr4qKt+3bAz2aoMErP43w8HBuu+02Xn/9debMmcOtt95KeLgsL5NIJJIznspK+PZb8dhFafm2bemcf/5/OXmykB49Eti0aSZXX92H885LpqZG4bPPdjh+4403Qtu2wv/7iy9cj8Wmn9sTXnhhA7m55fTqlchttw3x6L3O6Ns3iYgIA8XFVZabQVvUoHvChMYtLQfbTLcbQbc5010RHk1Njcg0yfJy52g0GvsScxuLKAClll1YsIioBTuubMMCG3SLTKxt0K16eZcmiKA78dhBACpircdCtEFLvrkFISjKy2sRLOXlYWF6y6SEqxLz9PQSSkqq0Ok0dO3qmSilVqthzBirirm7vPDCerp0edNa5eBp0O0q0x0Zaa2SaQol5gcOgMk8ubxvX2DHEkS4HXT//fffXHjhhRQV1RUvKCws5MILL2Tnzp0+HdyZgklRSC2pAiC1pApTPSUzEolEErSsXi1m4tu2hfMdl2X/9tsJRo/+jOzsMgYPbsOGDbdYBHJuu20wAB9/vN2xZUtoKDz0kHj88svOS9cUBTZuFI/d6OcuKKjg/fe3MnTox7z22mYAXnttvM+8nvV6LeecI9TPa5eYK4pi6R/01p+7IXiT6S7Sipv0yEgDoaENs+cyKQopxVXsy6skpbj5XQPVEvOcnLpBt9WjW4qoeYIr27BACamBNRNrW16uenm7ynSPbRdho14eBOXltQiW8nJwr69bndjs0iWekBCd0+WcMW6c2tftnpjaoUO5PP30bxw/XsBvv50QT0aaJ0/q0x9xp7wcmlaJ+Z491sd79wZuHEGG23cTr7/+OmPGjCHGVtHQTGxsLOPHj+e1117z6eDOBA4WVDJvbz6Lj4kf3eJjxczbm2/xbZRIJJImhW1puYOS7u++O8DFF39JcXEVo0d34tdfb7LzUJ06tQ8xMaEcO5ZvvXmpzW23iV7Yo0etXuC1OXBABI7h4TDEcbbaZFJYu/YY1123lDZtXufOO1fw55+n0Ou13HffuUya1N2TT14vQ4eKvu7aJYt79mSRkVFCRISB885z0wrNh6j7362g25zpLjQH3Q0thVevgfOPFLE8pZj5R4qa3TXQTsG8hX3WTfVGlyJqnqHuJ1fl5YHQGrCWl9sHhD3jQmnTWfz+Y9NFprIiNt7i1ZykKBiNIjMYiEy3Wl5eUFDhcLJTVS8PdKYb3LMN87a0XEXt696yJc0y4eCKf/3rV0vlT3GxeXlflpdD0xJT81XQfd55MGkSnDjR4CEFA24H3Vu2bOHyyy93+vrkyZPZvHmzTwZ1pnCwoJJvjxdTbLTv7ys2mvj2eHGzuumQSCRnAOXlsGyZeOygtPzTT7dz1VXfUFlZwxVX9OLHH6+rcxMXGRnCjBlC9MypoFpUFMyaJR6/+KLIatdCs2mTeDB0KITY98ieOFHAM8/8RpcubzJu3Bd8/fVuKiqq6du3Ja+/PoFTp2bz5puX+MS2yxZrX7d9plstLb/ggo4Nzhp7g5pZ86S8PM8kMmMNKS0/U66B1vLy0rqZbrN6ubW8XAbd7mAtL7fPdCuKEhTl5WlpRXWC1/jkNgBozOerrh1bcWffeHrGhVqy3HFxYQE5B6jl5SaTYg0abQiW8nKADh3ExIarTPfBg+L85alyuUrHjnF07RpPTY3C+vUpLpfdvj2dhQutgaUlSPelkBo0Ldsw26Db2/LyggLhPrJyJcQ2zD0kWHA76D516hTRLmZhoqKiSE9P98mgzgRMisLPaa5LTn5OK212ZXYSiSQIuekmOOssyM5u2HpWrhSz9h07wrBhdi8tX36QmTOXYzIp3HLLQBYtmkpYmOObS7WPeunS/VZ/49rcc4/IDOzeDStW1HlZ66Sf+8svd9Gly5s8++w6UlIKiY0N5Y47hvDnn7eye/edzJ493C7z7ktU27A9e7LsbmxXrz4KBKa0HDzMdJvLy7OrxUSGt9nEM+ka2LKlTaZblpf7BLX3vXZPd0lJlUVxOhBBd5s2UWi1GoxGk/g92dKqld1/Y9u0tHg1q5UOgchyg+iVVs/HjkrMg8WnG9zz6j5wQNiFeZvpBmu2uz7rsCee+MXu/x4H3Z5muptaefmJE9bP6Am7d4u/ycl1z5tNFLeD7pYtW3Lw4EGnrx84cIDERO8P7jON1BJjndn92hQbTaSWGBtpRBKJ5Ixkxw743//ERfL11xu2LrW0/JprhE+pDXPmCL/sW28dxCefXOayV3rw4DYMGtSaqqoavvxyl+OF4uPhrrvE4xdeqJPttmS6a/Vz//e/21EUkXX+6qsrSU9/kHnzLuWcc9r5PLNdm7Zto0lOjsFkUti69TQAFRXVlkxKIETUwHqjb2cZlptbd0FFsWS6s6oMgPeBzZl0DbQrL68tpFanvFwKqbmDs/JydZIuPFxPRISh0cdlMOgsEye2fd1AnaDbttUgMzNwdmEqVgXzumJqwZXpdr+n299B9/r1Kfz44xH0ei3TpgkHp+JiodHk80x3UykvLy6GFHN1gPqZ9u/3fD27zNf+AQN8M64gwO2ge9y4cbzwwgsOX1MUhRdffJFx48b5bGDNnVKje7P37i4nkUgkXvHuu/aPHQVb7lBSAj/8IB7XKi0/ciSPdetS0GjgX/8a5VZwqwqqffTRNud+rA88AGFh8McfsG6d5enw7Gw0J0+CTmeXcS8trWLjRpEl+N//rmDGjLMID2/cG/PaJeabN6dSXl5NmzZR9O3bslHHoqJmuvPzKzCqisqOMt3l5WC2ujpVJrJi3paXn0nXQGvQXS40BkJtAhdLebns6fYEdXIiK6uU6mrr5I0qohZI73hnfd2ug+7AVzqoYmqObMOCUUjNWU93WZnREpD37Ol90H3hhZ3QaGDv3mzLpJgtiqLw2GNrATGZPHiwaB+wZLrdEVKrrhb2WlB/pruplJer5eRt2sA559g/5wmqOHf//r4ZVxDgdtD95JNPsnv3boYOHco333zDzp072bVrFwsXLmTo0KHs3r2bJ554wp9jbVaovo2+Wk4ikUg8Ji8PvvpKPE5KEoHz3LnereuHH0RQ1rUrDB5s99J//yt8Oi+6qJul57E+RECsZ9++bH7/3YlXaqtW8I9/iMcvvmh5OkG9wA8ebJc9WL8+BaPRRMeOsXTr5pmNjK+oLaa2Zo0oLR83rovfM+3OiI8Pt1Qe5GrMwYqjoNuc5UanI724YXZhZ9I10K6nW6OxZLtr9HrL8amWScvycvdo2TICnU6DomBXxh3Ifm4VR7ZhgMugO9Dl5eDaNixYfLrBGnRnZpZSUVHXveLQITFxnJAQ3qDjICEhgkGDRCDtyDpsxYrDbN6cSni4nqeeGkV0tGi58ai83DYg9yTTbXJdJRRQ1NLyfv2gTx/x2BsxNTXTfSYG3V27duXnn3+mtLSU6dOnM3jwYAYNGsS1115LWVkZa9asoVu3bv4ca7NC9W10RbRBS3JU45dHSSSSM4RPPxWB8oAB8N574rm33rL07XrEggXi77RpdqXl1dUmi+f2P/4xyO3VxcaGcc01olzv44+dCKoBPPww6PWwZg389RcACeoFvlY/t9o7PWFC14AFuGqme/MfaezNrWDFT4Ht5wbhS6v2HWdWm3s2y8rq3iyqx0VcHLl54ibc24yir6+BwWw7ZtfTDZaguyomBjQaqqpqLBlaWV7uHjqd1lKKbdvXHQxBd/v24jv0LNOtlpcHLuhWy8tr93QrihJU5eUJCeGW1oE6Jfz4prRcxZl1mMmk8PjjIst9773n0rZttGXfeBR0q6Xlen0dwc86tGsnrq1VVQ3XX/EntkF3X3EN9zjorqmx9nSfieXlAGeffTZ79uxh27ZtLFiwgPnz57Nt2zb27NnDOWoJgcQtVN9GV4xrH2kR2ZBIJBKfUlNjDbTvuQemTBEXyKIiEXh7wl9/wfffi8fTp9u9tGrVEdLTS0hMjOCyy3p6tFq1xHzhwr3ObVs6doTrrhOPX3oJsMl01+rnXr1aZCsC1TsNENWlBVq9luzMUt5fdYw9OzIA6HRO+4CNCay9pOklGjCYA93arQZqpjs+3qaM17tMty+vgcFuO2bX0w32QTfW0mKDQWsJfCT146ivOxiCbquCeS07s7AwsLXddRB0B2N5eWmp0SKZEQxCahqNxmVfd0OVy20ZO1ZMhq5de8yuzWn+/N3s3p1FbGwojz56PmCdkPCop9tWRK2+c53BIEq2IbhLzB0F3Z6Wlx87JvZbWBg0o4SuR0G3ysCBA5k6dSrXXHMNAwcO9PGQzhx6xoUypXN0ndl+1bexZ1zgZxQlEkkzZdUqcWGLi4MZM4Sn9pNPitfmzhXBtztUVsLMmaLc7dprhQq6DZ98IkrLb7ihPyEhOo+GeN55yfTunUhZmZH583c7X/DRR8UNy7ffotmwgeg0czn6+edbFjl1qoh9+7LRaGDMmM4ejcNXHCyo5MeMCtp0FzeDv/73bxQFWndLYFO5EtAg0aJgnl3mXMFcDbrj4sjNbXhw44trYFOwHbMNuhVFsQRbatCt9nO3aiWUryXu4cg2zHpcBm7ywmlPN9hnu21E9azl5cEQdNuXl6vK5TqdhvDwxrczc4Srvm5fKJernH9+B0JCdKSmFnH4cB4AVVU1/OtfvwHwyCMjLBNlDcp011darlKfmFp2NsybB6++atHfaHQclZd7qmCulpb36yeqAJoJXgXdEt/RMy6UO/vGc3UXcfG4uku0xbdRIpFI/MY774i///iH9eZg6lTo2VMEV7YCa6548UVxkW3Zsk6GPDOzhB9+OGTejPul5SoajYZbb7UKqjmld2+48koAdDffDIDSuzckWDMdqhf2Oee0C0g20dYiK/ms1gDs/UWMqZu55DyQFlkOFcxrB91qebldprthGUX1Gnhttxgu6xjNtd1i3L4GNhXbMTXoNhpN4obcHGxVmoNuq3K57Of2BHV/OSovD6yQmpOebrAG3ZGRdoJ6arVDMJaXW0XUwgLWllMbV17dviwvj4gwMGKECHRVFfNPPtnGsWP5tGoVyaxZQy3LRkfXCrrdEVJz1y5MxZFtWHExfPEFXHKJyITfdZeYiH72WffW6UtycyFDVG/Rp4+4liQlif97omDeDEXUQAbdQYFWoyE5SvRyJEeFyJJyiUTiXw4fFplujQbuvNP6vE4HqiDmnDn1z0zv2GEVMHv3XWuwZuZ//9tJdbWJoUPb0bdvkldDvfHGARgMWv7+O53t29OdL/jYYwBozBkAU53ScrWfOzC907YWWcn97Hs7uw0VN1KBtMhyy6vbprzcGtw0fAJDq9HQMTqEPi1C6Rjt/jWwqdiOhYcbiIwUJfs5OWUOMt0iaJT93J6h7i/78nIRMAa2p1t8r6dPF1NTU+v4VINum9Jyk0mxiMEFtrzcsZBaMPVzq1i9uu2DbpNJsQipNUS53JaxY0Vl1Nq1xykrM/Lvf68H4MknLyAy0tqHbS0v92OmW1UwP3IEvvtO2HMmJcGNN4prek2NtaT7xRftXD0aBbV3u1Mn62fypsS8GYqogQy6JRKJ5MxD7eWeOFGojdty7bXiuZwceP995+swGkVZeXW1yDJffbXdy4qiWErL1Wy1NyQmRjBlSm+gHkG1IUPgoous2x8xwvLYZFIsWYrx4wPTz21rfdWhvzXo1hm0dB7c1uFyjYk10+0i6DZnuo1RMRbV4EBmFJuS7ZhdX/f06ZjOOYc0s9CfWh7dunXgspxNEWum2zo5GAw93W3aRKHTaaiuNll6tS04CLrz88sxmieP1MmvQOCspzsYg25rebl90J2WVkRZmRGDQUvnznE+2Zbq1/3LL8eZO/cP0tNL6NQpjn/+c4jdcrY93SaT4nlPtzuome533hE6LIsWCcuxnj1FZvvQIVF5NnMmKApcf71wKWksbEvLVbxRMFcz3c1IRA1k0C2RSCRnFiUlQrUchIBabfR6ePxx8fg//3F+w/Daa7B9u7h5fPfdOiIwmzencvBgLpGRBqZN69ugIauCal99tZuyMhdZS3XcgGLTz71zZwbZ2WVERYVY1MMbG1vrq4TkWMLNfrcdB7QhxMYrPFAWWZ5kustDRbCj12stNjmBoCnZjrVsKfZvdnYZDBtGzaZN5PfqBdiWl8tMtyc46ukOhqBbp9NaxuZUwdyBiFpcXBihoYHrX62/vDz4gu7amW61tLxbtxYYDJ5piDhjyJC2xMaGUlBQwTPP/AbAs8+OrqNRYnsuLCmp8izodjfT3dfmWtquHTz0EGzbJkq3//Uv6N5dvPbmm9CjB6SlwW23QWO12DgKuj1VMC8sFD3gIDPdAAUFBaxevZovv/yS//3vf3b/JBKJRBLEfPWVuKh16wYTJjhe5oYbhCp4ZiZ89FHd1/futfaLvfkmtG5dZ5GPPxZZ7muu6WvpdfOWMWM607lzHIWFlSxe7KJEbeRIal54gT233GLNCGDt5x49upPHYm6+wtYiS6PR0LG/UKHtcV4HyzKBtIlUBZxcZrrNQXeJXtxMJiSEB7THsylZb9ZRMLfBmumWPd2e4Ki83BcCf75ALTGv09fdo4f428Xa5qL2cwf6+29K5eW2QbetqrhVudw3peUgJhdHj+4ECF2Gvn1bct11Z9VZLixMj14vzkfFxZX+KS8fNw6WLIHffhN93a+9BoMG1VU+j4qCr78WiudLlzq+jvsDV0G3u+Xl6jrat7ebnGoOeBx0f//993To0IFLLrmEe+65h1mzZln+3X///X4YokQikfgOd/x8g9nzt0EoilUg7e67hWK5IwwGS480r76Kqbzcuj8KylFmzhReoZMmWe26bCgqquSbb8SstjcCarXRajWW9bgUVNNoMD38MEcvv9zu6UD3c0Ndi6xJD45g3J3nct5060x+IG0i3cp0m8vLi3Ti5jzQgU1Tst50HXSrPd0y6PYEdX9lZpZQU2NCURSfag3UxpPrgtU2rFbQffXV8O238MorlqeCwaMbXJWXCwusYAq627ePQaOBysoaUT1ixiqi1nC7MFvUEnOAF14Yg05X99qp0WjsFczVoNuXQmoajWjnGjXK+fVbZcgQq+bK/fd7JmTmDYriurzcXQXzZiqiBl4E3Q8++CAzZ86kuLiYgoIC8vPzLf/yGrNvQCKRSDzEHT/fYPf8bRAbNsDu3eJmwKzy7ZSbbxYzzadPs+GFdyz74+hzr6H5809qomNEz7eDgGbhwj2UlRnp2TOB885LrrtuL7jllkHodBo2bjzJ/v3Zbr+vrMzIhg1C6TWQ/txgb5GV2CGOsbedgyFMHxQ2kbZBt6mF+YbVSaY7zyRuzgPZz63SVKw3W7YU+yo7u+4NuCwv945WraLQaKCmRgTbxcVVlt5oXx+bnl4XnNqG6fVwxRV2zgrBYBcG1vLywsJKOwG4wkKR+Q6m8vKQEJ3l92JrG+ZLuzBbLr+8JzExoVx0UVcuu6yn0+XUEvOiokqrerkvM92eMns2jB8P5eVCr6Wiov73eEtGhrhGaLWix1zFUwXzZiqiBl4E3adOneK+++4jIiLwF1uJRCJxF3f8fJuC52+DUG3CbrhB+HO7IjSUzHsfBGDgJ2+iNVYRn3KUke+9BMBPDzzLwaiWDt+qCqj94x+DfFZ+3LZtNJMmidLMefO2uv2+DRtSqKqqITk5hh49fJv98IaGWGT5EzXorq42URIuAgZnme5ckxirP7KJ3hCs+9QWZ5luRVEsQVegy4ubGnq91nLcpqeXWPZtRISBiAjftRR4c12wBt0ObMNqEQx2YSB6ylUKCqzBWXFx8GW6wXFftz/Ky0FULmRmPsQPP8xweU1zmOn2pZCap2i18PnnIvDdudNaweYP1Cx39+4QFmb/micl5s1URA28CLovuugitm51/4ZHIpFIAo07fr5rUkuahOev15w6JXq7QJSW27Bu3Qk++WSbXW+cSVH4dsxUShKTiM04Rf9l85n47CwMlRUcHzqKXZdf53B/7N2bxZYtp9Drtdx4o28vmvfeey4gSszVG9X6sJaWdw0aj1lvLbL8SUiIznLTnYv5ZtFJpjvTKLI5wRJ0Q3DuU1usQbd96W5enlW5WgbdnmPb1+0PETVvveDVnu465eUOUMvLA/39Gww6osz2tbZ93bY+3cFEx472QXdxcSWnTonMcc+evp9gte3Zdoatgrkl6K6sFFZejvB3phuEd/dnn4nHc+fCjz/6ZzuOSstV3FUwN5lENR7ITDfApEmTePjhh3nmmWdYsmQJy5cvt/snkUgkwYY7fr4l1UqT8Pz1mg8+EBf+Cy6As6wiMLm5ZUya9DW33vo93313wPJ8aomRAl0oW268F4Bx/3mC5B1bqAqPYNVTc0Cjcbg/1Cz3pZf28Hm55NixnRk6tB0VFdW8/vrvbr1n9Wohohbo0vKmgMU2rMZ8c52TY696a850Z5QLheVgKC9vKqiBYO3yclVELSEhPGAif00Zq4J5sV9E1Lz1gld7ut3JdFvLywNvGedIwbywMPiE1KCubdjBg6K0vFWrSIsoXGPjMNMNorzbEf7OdKtMmgT3ims5N98sRFJ9jaug210F82PHRA98aKhVdLAZ4XHQfdttt5Gamspzzz3H1KlTueKKKyz/pkyZ4o8xSiQSSYPwpU9vMHj+ekxlpQi6oY5N2Ntv/0lpqbhh/Pe/11uy3ern3H7VjZTGJ6KvEjdev856msK2VsVt2/1RVVXDF1+IfixfCKjVRqPR8NRTwtv4vff+stxkOyM9vZg9e7LQaETALnGNWqp7qtJ8c11VZb0prK62ZGVOlYmgO9BCak0Jtae7dnl5RkZwZDmbKrZe3f4QUfPWC14tLz99upjqatdBu1VILfDHgCMxteLi4A661Uy3v0rLPUF16igqqrQvsXYmpuapZVhDePVVMeGelQU33SSyyr7EnaC7vvJytZ+7b1+hf9DM8DjoNplMTv/VOCufkEgkkgDiS5/eYPD89ZglS8SFtm1bIeJjpri4krfe2mL5//btGaxceRiwfs7q8Ai23CxmyE8OOY/tV99st2rb/bF8+UFycspo0yaKiy/u5pePMnFidwYNak1pqZG5c/9wuaxqFTZkSFuZlXUD9aY/vdAE4ebARS0xN2e5AdLMFZHBVF4e7Djr6ZYiag1DDbr9VV7urRd8q1ZR6PVaTCbFok7vjGCxDAPHtmGqenkwCalB3aDbX8rlnhATI8rzi4srRT91fX3djVFerhIWBvPni78//SSEUH2FyWTNYrsqL69PwbwZi6iBlz7dEolE0pRwx883Sq8JPs/f4mK47TZxcWzIpKYqoHbHHcIOzMwHH/xNfn4FPXokMHv2MMCa7bbdZ39edwffvD2fxXO/srMpqb0/1NLym28eWG/vm7doNBqefFJku9966087wZ/aBINVWFMiKUncINrZhuWKkk1L0B0VRWaeuAmXExnuowaC+fkVdplPa9Ad+ICrKaJOVthmun0ZdHvrBa/VamjXTozNVV+3yaQEjWUYOCsvF+fYYMt01+7p9pdyuSfYlZdD/UF3Y5WXq/TtC888Ix4vWeK79Z48KbL5ISHQzcGEu62C+YEDdV9XacYiauBl0L1u3TomT55Mt27d6N69O5dddhkbNmzw9dgkEonEJ7jj5zs+Ocptz99G8/FetAg+/hjuvBPOPhs2b/bs/WVlwg/2999FsH3bbZaXbPui/+//RvDIIyMID9ezZcsp1qw5Zr/PtFqOjRhHVaR9YGDrgZyaWshPPx0BYOZM35eW23LFFb3o27clRUWVvPPOnw6XMZkUfv5ZZLrHj5f93O6gZrozM0vqenWbRdSIjyc3V9yQy0y3+7RoEW5x17Nti5DK5Q3DtqfbVdDt7Tm7IV7w7vR15+eXWyZh1PaOQOK4vFzNdPtWSK2h11E1052dXUZZmTH4ysvB95luUw1k/gYn5ou/Ji8m4weZr8/Z7ltv1otaWt6rl/OycHf6umWm254vv/yScePGERERwX333cc999xDeHg4Y8eO5euvv/bHGCUSiaTBuOPn684yjerj/buNWNiOHTBihOjFyshw/b6yMpgzBzp3hv/7P/HczJnQurVlkc8+20FGRgnJyTFcd11/WrWK4p//HAJYs92eeCB/9tkOFAVGjepIt24tGvSx60Or1fDEEyMBeOONPyw9h7bs3p1FZmYpkZEGhg9v79fxNBesXt1ldYNuNdMdF2cJGmWm2310Oq0li2hbYq72dMtMt3fYlperk0G1g+6GnrO99YJ36tVtg5rljo8PIzQ08D2s1qDbWkHkDyE1X1xH4+LCLGrrJ04UcOhQMGW6xUSFTzPdqUtheSdYeyFsniH+Lu8knveElmarz9ruFA3BVT+3Sn0K5kVFQkgNmm3Q7fEv/IUXXuDVV1/lgQcesDw3a9Ys5syZw7///W9mzJjh0wFKJBKJr+gZF0r32BBSS4yUGhUiDRqSowx2WQpXy6h+rbVR/VqndMa33sB/mHuWP/pIPP7kE/jf/+C77+DZZ4X1l025OGVlQjDtlVes6qSdO8NTTwlvbjPV1SZeeWUTAA8/fJ5FNfmRR0bw/vtb2bjxJOvWpTB6dCe39ll+fjkffPA3ALfeOth3n98F11zTl6ef/o3Dh/N4//2tPPzwCLvXf/75OACjR3cKipvZpoBFvTyzBDo5znQrcfEU7hY3xjLT7RmJiRHk5paTk1NGjx7xgOzpbijqfsvIKCE7u66Qmq/O2e6cB2uj2oa5ynRblcuDY9Kldnl5TY1CSYlvfbp99Z1oNBo6dIhl375sNmxIobKyhtBQnaXsPBBYLcPcyHQrivuZ7tSlsOFqoFY1QNkp8fzIxZB8pXuDtA26FQV8Ya/oTtBdn5iauo62ba2Tvs0MjzPdx44dY/LkyXWev+yyyzh+/LhPBiWRSCT+wh0/X0fLeOvX6jWFhdYZ4cmTRZn5li2izLyoCB54AAYPht9+Exf0N96ALl1g9mwRcHfuLIL0gwfhllvsSr4WLNjDiRMFtGwZwT/+YQ2S27aNtqiOP/fcOpf7Q8VkUrjxxu84daqYzp3juOqq3r75/PWg02l5/HGR7f7Pf36nrMzesmftWnE9Gj9e9nO7izXTXeq0vLwqQgQ5Gg0Bs+VpqrRsKfavGhyC1TJMlpd7h7rfjEaTpbxYzXT7+pztqRe8mul21dOtiqgFQz831BVSq6iw6g/4QkjN19+JGmCropnduyeg0wVOrqpOT3ek+Xt1pF5u69/tKtNtqoG/Z1En4Abrc3/f736puRp0G43iPsMXeBJ0O8t0N/PScvAi6E5OTmbt2rV1nl+7di3Jyck+GZREIpEEG976tXrNX3+JWejOnaFVK/HcueeKjPeHH0JCgrjQXXghtGtnDbY7dRIB+sGDoqTcYC/wYzIpvPTSRgAeeGAYERH2rz/66PkYDFp+/fUEmzadrHeYL764gR9+OERoqI7Fi68hPLzxhOauu+4sOnWKIyurlI8/3mZ5vrLSxIYNYuzSn9t9rD3dDoJuc3l5eZhYJi4uzG9iec0VRwrmanmxLC/3jpAQnWW/qvtS/X+jn7Nr4U5PtzrmYJl0qd3TXVYmArmQEJ1PKoZ8/Z2ofd3qJGsgS8sBoqNFubtbPd22Kt6RLiZdsjdAWZqLrSpQliqWc4fQUGuQ74u+7upq2L9fPHanvNyZgnkzF1EDL4LuBx98kPvuu48777yTL774gi+//JI77riDWbNm8dBDD/ljjBKJRBJwvPVr9Rq1tHzYMPvndTohiHbwoBBY02hEQKQG24cOwT/+USfYVlm+/CD79mUTExPKXXedU+f1Dh1iufnmgYDo7XbF6tVH+de/fgXgvfcmMXhwG08+YYMxGHT83/+JsvJXX91EZWU1APv3l1JZWUP79jEBvwlrSqiZ7pKSKqqi48STtTLdpQZxEyn7uT0nMdG+p7uy0mS5OQ+WoKspUnvCQg26G/2cXQt3erqt5eXBkemuXV6uBt2+Ki339XeiBt2qi0Ug7cLAtrzcjZ5utbQ8PFxc151Rnu7ext1dDqzZbl8E3UePQlWV+KwdOzpfrj4Fc5nprsudd97JggUL2L17N/fffz+zZs1iz549LFy4kNtvv90fY5RIJJKA461fq9eoImrDhzt+PSEB3ntPCKwtWiSCcBfBNoCiKLz4opgNv+eec5yq0f7f/52PTqfhp5+O8uefpxwuk5JSwIwZS1AUuPXWQX5XLHfGzTcPpH37GE6dKubTT3cAsGOHyCyNH98FjS/61c4QoqNDCAsT2awCgzmQqZXpLtKL4ED2c3uOtbxcZDfz8kQ2LzxcH3R2TE0JVcFcRZ0QavRzdi3Unu6MjBKMRselv1a7sOCYdKldXl5eLrLSvvLo9vV3ogbdKoFULgcPLcMciKilpBSwfPlBFNvy+nA3J7PdXQ6slUy+CLrV0vK+fe0sRR3irMTcZLIG3TLTbc+UKVPYuHEjubm55ObmsnHjRi6//HJfj00ikUiCBm/9Wr1CUZxnumvTvz9cfbXwx6yHtWuP89dfpwkP13P//c7X26VLPNdfL2abHWW7KyurmTp1Ebm55QwZ0oa3355Y77b9RWionkceOQ+Al1/eiNFYw44dIoMgS8s9Q6PRWLLdeRpz5q1WprsAMVEjM92eYy0vF1nEggIRdLdpEy0nhxqArQhdZKTBMnHUqOdsB7RsGUlIiA5FEerqjgi+nm778vLSUt9mun39ndQWTQt0ZZNHlmEORNRmzlzO5ZcvYONGm9auliMhoj3g7ByhgYhksZy7+DLT7U4/t4ozBXO15DwkBHr0aPiYghTZkCWRSCRu0BC/Vo85cgTy8kTvlQ9nfdUs9z//OcSSdXPG44+PRKvV8MMPh9i+3b5sbdasVfz112latAhn8eJrLDe5geLWWwfTqlUkKSmFvPHGFk6cqECjgXHjAiSi5gsv1QBhUTCvMVdB1Aq680ziptKRF7LENeo+y84pJbWkivx80Q7RurVnAVdD/Y2bG7bl5bbHZaOesx2g1WrqVTAPtp5utby8tNRIVVUN5eXi3OUrj25ffyd1M93BUV5eUVEtqhtcCak5yHSfOFEAwLZtNtdcrQ6GvGn+T+39Yv7/kLliOXcJVNDtTMFczXL36eOyWq+p41bQ3aJFC3LMF974+HhatGjh9J9EIpE0Vzzya62uhnnzYPt2zzeklpYPGeJWBtu9Vaby668nMBi0PPigk5J1G3r0SGD6dHERtc12f/75Dj744G80Gvjqqyvp1CnOJ+NrCOHhBh56SGS7n35aqK4PHNg6MIGhr7xUA4Sa6T5dZT6WVVsZc3l5drU4HmV5ueeox+OB1GIWHyu2lJeXRYa67U/sC3/j5oazoBu899j2FfX1dQebZVhsbJjFQSo/v9znmW7w7XfStm00Wq3G8ljNNAcKVUgNzH3dHma61QqDgwdz7ZdNvlLYgkW0s38+or1ndmEqgQ66a2e6zwARNXDTp/uNN94g2jwT88Ybb8gyKIlEcsbitl/rc8/Bv/8tSqUOHPDMC9NcWl415Fxuv2UZI0Yk849/DGrQuVdVLL/xxgEWVd36eOKJkcyfv5tvvz3A7t2Z1NQo3HHHCgCefnoUF1/czevx+Jo77jibl1/eSG6uuGkZN65z4w/Cl16qAULNdJ8sNd8e1NQIWxlzpjujQg8YZdDtBWVhIoNTlK+Wl4tMd3iLCLf8iX3lb9zcsO3pdjTR5o3Htq9QM92ObMNMJkXY8xE85eVarYbY2DAKCirIz6+w9HT7WnPAV9+JwaCjbdto0tKKAl5aro4nLExPRUU1RUWVtPCgp9tkUiyCcHWCbhDXjnaXC5Xy8nTRw91ypGcZbhVfBd2VlXD4sHjsSXm5Wk6uTjicASJq4GbQfdNNN1ke33zzzf4ai0QikTQJVL9Wp/z+O7zwgnh86JAQOevVy/0NmIPu9VWt+eyzHXz22Q6WLNnPJ59cVkc0yB127crk++8PodVqePTREW6/r0+fllx9dR8WLdrHY4+tZf/+HCoqqrnkkm489dQoj8fhT6KiQnjggWE8+aRQUx8/vpGD7nq9VDXCS7Xd5d7dJDUSaqY7Pb9a3BCVlIhstznTfbrcgAi6ZXm5J5gUhb1V4tgoM99Y5+eLTHe0eV/+nFZK91jH/s/u+hs7e39zxran21l1S73nbD9hzXTXDbrz88uprhZBrfq7Cwbi40XQXVBQYVEv95WQmi2++k46dowlLa0o4KXlKjExoZag2y0hNXPgWVRUidopcuBAjuOVa3XQanTDB6kG3TlOtuMuBw+Kidm4OGjjhpCbqmCelSWSEWefLZ4/QzLdHvd063Q6srKy6jyfm5uLzpXkvUQikZwJlJTADTcINU715veHH9x/f2mpZdb3+2xry86qVUc466x5LFq019k7nfLyyyLLPXVqH7p39+zG5MknLwBgxYrDHDuWT6dOcXz55ZWWkr5g4p57zqV9+2hatjQwfHj7xt24r71UA4R682/n1Z2dbcl0p5aI67zMdHtGaokRokTgYqyopqrCaA26zYGiK3/iQHtOBzO25eXBdly68upWS8vj48N84oHtK6y2YRU+twzzB336iACysS0rnWG1Dasn6K5VXq6WloMQ3isu9mPLiK8y3bal5e5O9tUuMS8pEbZj0Owz3R4H3YoTwY7KykpCfNR7KJFIJE2W2bPFBaRDB1FiDvD99+6/f+tWMXPcvj3Lt4kL9ZtvXszgwW3IyyvnmmsWc/31Sy1laK5ISyviP//ZzMKF4uL22GPne/xx+vdvxRVXiCx9aKiOJUuusdyUBRuxsWFs334bb73Vq/FvYv3hpRoA1N7SrCyboDslRRyTwElz7CAz3Z5RalQIiTCgDxGTFqX5FRYhtWgbUUNn/sSB9pwOZtzJdAcKV+XlwWYXpmJrG1ZW5p/ycl/y4otjWbDgKm68MTiypHa2YR4Iqak2bSqHDjkoMfcV/gi63aW2grm6jtatreNqprh9V/LWW28BwlLk448/JsqmWwcl+QAAkuRJREFU8b+mpob169fTy5PySYlEImluLF8OH30kZnw//xw6doSnnoJNm4QauTtik+bS8rIBQzixogCdTsMttwzkjjvO5t//XseLL27kq692s25dCp9/fgVjxtiXUWdnl7J48T4WLNjLhg0plnK1K6/szYABrb36WK++Oo6CggruueecoMkmOCM2Nozw8ABUXfnDSzUAWDPdJZBsDrrVnj2DgbQ8EXwHW3AT7EQaNGg0GiLiwijKKqW0oKJOebm6nLP3u7udM42wMD3x8WHk51cE3XHpSkhNtQsLFuVyFdU2rKCg3K/l5b4iMTGCadM8CPr8jCqmVm95uYtMN4gS8yFD2vpnkIEMumsrmJ8hpeXgQdD9xhtvACLT/f7779uVkoeEhNCpUyfef/99349QIpFImgKZmXDrreLxgw/C6NHicd++YkZ31SqYMaP+9ZiVyw/GCZGygQNbWxRZ//3vMUyc2J0bb/yOI0fyGDv2f9x//1D+7//OZ9WqIyxYsJc1a45SU2PNdo0c2YFrr+3HzTcP9Pqjde+ewK+/3lT/gmcyqpdq2Skc93VrxOueeKkGAFXQKSurFAbZB91KXBy5eeLGMNjKeIMd1Z840hx0F+eWUVhoznSbA0VX/sTq+12VmPvTczrYadMmmvz8iqCrwFDLyzMzS6msrLarwLEqlwdPPzc0vfLyYMNaXl4F7d0XUqud6XYopuYr1Cqm8nKRhY/08hhsSNCtZrrPEBE18CDoPn78OAAXXnghS5cuJT4+3m+DkkgkkiaFooiAOzsbzjoLnn/e+trkyeLi8sMP9QfdimLJdK8taw2UMnJkB7tFhg9PZvv223n44dW8//7fzJ27hblzt9gtM2RIG669th/XXNPXbaXypoBJUQKiQOwWqpfqhqsR3qm2gbeXXqoBQM105+SUYWqRIHrQjhwBwBQbR022+FyW4MZU4xs13WaO6k8811y6m3UsH0UBjVZDpPk5V/7E6vsdqZer+NNzOti55ZaBfPrpDkaN6hjoodiRkBBuUbM+daqYLl2s987W8vLgCrqtmW6rermvfLrPBOzKyxuQ6fZr0B0VBaGhQn08O9u7oLukBMyxoSWQdofaCuYy0+2cX3/91R/jkEgkkqbLxx+LoDokBL76SlzMEIFL3MWXoH/5ZfjxRzAaweAiE5WSIjLmBgMLD4nlRo6sexMZFRXCvHmXMnlyT/7xj+VkZJTQu3ci117bj2nT+tGjR3CouPqSgwWV/JxWapfpizZoGdc+MnhsklQv1b9n2YuqRbQXAXeQ24WBKNXUaMT8T1lELFFgCbqNkSIjExFhICxMLyzSHH7WN5vEZ21sesaF0qNdNEeAjCN5AETFhxMbpnfrOBb+xgT/7yAAPPTQeTz00HmBHkYdNBoN7dvHcORIHmlpRU6C7mArL7f2dPvDp7u5Y19ebv5uPch0JySEk5tb7lzB3BdoNKLEPC1NBN2dOnm+jv37xd/Wra2Zc3ewVTDfv19muusjLS2N5cuXc/LkSaqqquxemzNnjk8GJpFIJE2CI0fggQfE4xdfFJluYOXKw0yZspDLL+3GNwkJkJsLmzfDKBdWW+bS8uqz+vP39gIAzj+/g9PFJ07szqFD95CeXkL37i0a5OMdzDQpf2JfeqkGAJ1OS2JiBNnZZRToo0TQbe77qwgXN4cJCeHNwpM8EHRpK27CK9NEj2+HdlHc2Tfe7Qx1ID2nJd6RnCyC7tp93Wp5ebD1dFvLy8spL5dBt6fYZ7qTxJOuhNRqZbqHDWvPihWHOXw4F5NJ8Z9TiG3Q7Q1qabknWW6Vvn1F0L1ypcj4GwzQs6d342hCeBx0r127lssuu4zOnTtz8OBB+vXrx4kTJ1AUhcGDB/tjjBKJRBKcVFfD9deLC+qFF1qC7yNH8pgxYwlVVTUsXXaIyqsvInTh10LF3FXQbS4tT23XB2Ub9OiRUK9/a3R0qKXnuznSJP2JfeWlGiBatYoiO7uMPG0ktsZrZaHi5jAxMaJZeJIHgpZmpfIj5tLR5LbRHh+3gfKclniHM9swVUgtmMvLVfXyYBZSCzbsLMPUsm23ystFpnvQoNasXn2U8vJqUlML6dgxzj8DbaiYmjf93Cp9+sCvv8L8+db/nwEOWB5bhj322GM8+OCD7Nmzh7CwMJYsWUJqaiqjRo1i6tSp/hijRCKRBCcvvQRbtkBsLHz2GWi1lJRUccUVCygsFB6bNTUKf7UyT0jW59dtDrp/V0SoU7uf+0xE+hM3PhYF8xr7Ps4SnehPTIiuaBae5IFAVdeurBQZxGDLckp8T/v2okKktm1YsJeXZ2WVUVkZ/JZhwYY6CV5UVGXt6a6oAFOt65iT8vLExAi6dxctYn7t61aD7hwvy9gbEnSr2fGDB8XfM6C0HLwIuvfv389NNwkVW71eT3l5OVFRUTz33HO88sorPh+gRCKRBCV//QXPPisev/sudOiAoijccssy9u7NpnXrKG66SQiDfHqqJej14gKj2i/VpqICtm8HYOlpkRmRQbf0Jw4EauYt3Wh/o12gFTfjCTE17q0oyD3JA0FtS6vWrYMryynxPY4y3SaTErSWYWp5uW05vAy63cehkBoIpXBbnAipxceH07OnCLr92tcdyEx37ZL0M0BEDbwIuiMjI6msFBmctm3bcvToUctrOd7OlkgkEklTIjcXpk2Dmhrx16xK/uqrm1i8eB8Gg5YlS67htttEhnvp2nRMF1wg3uss271tGxiNKEmtWL5LnGMdiaidaUh/4sZHzXSnltuX++WbxM1kQmK0eysKck/yQNCyZe2gO7gCLonvceTVnZdXbrF2rK+FqLFRy8vVaozwcD0Gg2wTcRe78vJwG2vF2iXmTjLd8fFhlqD74MEgDbrz8+H0afFYVSP3hNrvkZluxwwbNoxNmzYBMGnSJB588EFeeOEFZs6cybBhw3w+QIlEIgkqqqvhmmuEVUaXLjBvHmg0/PTTER57bC0Ab799Ceedl8ywYe1JTIygoKCCY71HiPc7C7rNpeW53ftjrFZo0yaKzp3jGuEDBTeqP7ErzmR/Yn+gZrpPFNnv95wac9DdvotQKcfZRIcGIpKD3pM8ENTNdMugu7njKNOtZrnj48MICQmugFYtL1eRWW7PsMt0a7UQZm7TsRVTM5ms/3eY6RZq4I1SXu5N0K16bHfsCDExnr9fVTBXkUG3Y+bMmcPQoUMBeOaZZxg/fjwLFy6kY8eOfPLJJz4foEQiCTzCH1k4FaSWVGFSzuBS3ocegl9+EQIpy5ZBfDzHjuVz7bVLzHbdg/jnP4cAQgl60qTuAHxT3lm8f/16KCzEpCikFFexL6+SlOIqFLNy+a4IsdzIkR2brRq5J6j+xK44k/2J/YGaeUvPqYS4OMvzmVUi852QGClswYC6gXfT8SQPBLWD7jZtZNBdH7XPlU3t+tO+vQhKcnLKqKioBoJXuRyE5ZVOZ/1dy6C7fmyP0RKz2nhRkahYcyimZhuAO8h09+olgm6/lperNl/eBN379om/3iiXq6jvbdVK/DsD8Fi9vEuXLpbHERERvPfee15vfN68ecybN48TJ04A0LdvX/71r39xySWXAKAoCs8++ywffvgh+fn5DB06lHfffZe+DfmSJRKJR6j+yKWVVfQEFh8rJjK00j++sDU1MGsWHDokZk+jox3/bdMGRo8Ws8iNyeefw5vmYOOLL6BfP0pLq5gyZSH5+RUMHdqOd96ZaBcsT57cg88/38mn60t4rFcvNAcOcHrpD3x7ziV2AmF3b9hMNLCqQFwIZT+3FelP3Liowk6ZmaXixqygAICMCnHLkJgY0Sw8yQNBQoJ90B1sytXBhnr9acq/+/j4MCIiDJSVGUlLK6JbtxZBK6IGwls8Pj6cnBwRJMqg2zW1j9GsfCHqWaAG3RERoiXNNuhWS8vNmXCTSaGgQA26w2nbVgTip04VU1JSRVSUH5S9G5LpPnlS/O3c2fvtqwrmZ0iWG7wIuv/66y9MJpMl262yZcsWdDodZ599ttvrat++PS+//DLdunUD4PPPP+fyyy9n+/bt9O3bl1dffZU5c+bw2Wef0aNHD55//nnGjx/PwYMHiY52s6dMIpF4ja0/sm146zd/5K++EqJk7vDJJzBzpu+2XR9btsDtt4vHTz8NU6agKAq33vo9u3Zl0qpVJEuWXENoqP1pdcKEroSE6DhyJI+8meNIOHCAvMXLKB54kWWZ6MzTRGeexqTV8tl+cXGVQbc90p+48VAz3VlZpdAmQXjRA6kl4ixgCRybuCd5IAgJ0REbG2pxN5CZbufYXn9s8dv1x09oNBqSk2M4eDCX1NRCc9AdnHZhKvHxYTZBd/O3cvIWR8doqDlALi6u4kB+Bb1UMTXboNtWRE2jobioApNJVHDEx4cRHm6gZcsIsrPLOHQol8GD/aCP0ZCgO8080dq+vevlXDFtmnB9uf5679fRxPA4TXT33XeTmppa5/lTp05x9913e7SuyZMnM3HiRHr06EGPHj144YUXiIqK4o8//kBRFObOncsTTzzBlVdeSb9+/fj8888pKyvj66+/9nTYEonEQ9z1R/ZZqZ/RSM2/ngHgv5rBPN/6Gr7uP4NNF1zHsYnXUTRlGsoVV1hnRT/91DfbdYf0dLjySqishCuugH/9C4A5c35nwYI96PVaFi2aSrt2dXuboqNDGT26EwA/GXoB0HXTz2hqrArQbXdvBeBUcg+yS0RmoV+/pDrrOtNR/Yn7tAilY3QQ+XI3M9RAIDOzBEUtQQRSi9Wg26bnU/Uk73St+CsD7npRS8wjIrSEh0stAkc0+vXHz9Tu61Yz3cFYXg5WBXOQmW5nODtGQyNE0G2qNvHj0SIUR0G3ExG10FCd5Zzg9xJzNeguKhL3Np7gi6B75EixH2680ft1NDE8znTv27ePwYMH13l+0KBB7FNr/L2gpqaGRYsWUVpayvDhwzl+/DgZGRlMmDDBskxoaCijRo1i8+bN3K5mnGpRWVlpUVcHKCoSJzij0YjRGLw+rurYgnmMkjOL1JIqSiurLDNzWlO13V+A0ko4UVBGsg9KnzSffII+5TiZRHKvcjFlGSGQYb9MeLieUV2HsoLdaDduxHj0KHTwc0a4shLdlCloT59G6dOH6k8+gZoa1q4+wiOP/AzA66+PY9iwtk5/vxMndmX16qO8vdPAlJg4wgvzab/zD04NFBVD7Xb9BcCeuG6QAoPObYvJVIPJ5KY1k8SCPJc2nPh48XuurKyhMjoW1a37eIH4GxNjkPu3ASQkhHP0aD7x8XI/OqP29ccRvrz++Ju2bUVwnZKSj9Fo5PRpcW+amBgelMdAnE0FQVSUPE4d4ewYDQuzTgbn5ZVQGRJGGFBdVIRi3o+a/Hz0gBIZSbXRSFaWyHzHx4dZ9nX37i3YsOEk+/Zl+Wf/R0ai1+nQ1NRgTE+Hdu3cfqs+NRUNUN2qleUzBYpguOa7u22Pg+7Q0FAyMzPtersB0tPT0es9Xh27d+9m+PDhVFRUEBUVxbfffkufPn3YvHkzAK1qNde3atWKlJQUp+t76aWXeFb1zrVh9erVREREOHhHcLFmzZpAD0EisdDTwXPdT/9t9//dabC7gdvRGo2MfvxJooGXOJ+7H+5BWJiWkycrOHmynJSUCtLSKigvr2bVnmrW04HRpHDouec4cqUfe0cVhYHvvEPHLVuoioxk/b33UrphA0ePlvHUU0cwmRTGjm1Bhw6ZrFy50ulqIiKECN2ff6aTMbQfnX/fyLkr/8c+cxKx69/rANhaKjLlya0rXK5PUj/yXNowwsO1lJebOJBdxEDzc2pP999/b+TAAZnR9haTSVWuNsjj1AWOrj+18cX1pzGoqMgEYPPmPaxcWcjevScAyMw8ysqVBYEbmBPKyvIsjwsLXV/fzmScHaPq+bPdsa0UVZYTBuzcvJm0UDGZ0WrrVoYBBSYT61euZNcuEXTr9dWWfW0yZQGwbt0eVq4s8cv4L4qOJqyggI3ffktRrbjOKYrCpJMn0QO/HTlCaW0rtAARyHNpmZv7wOMoefz48Tz22GMsW7aM2FhRLlNQUMDjjz/O+PHjPV0dPXv2ZMeOHRQUFLBkyRJuuukm1q1bZ3m9tnqvoiguFX0fe+wxZs+ebfl/UVERycnJTJgwgRhvZO0bCaPRyJo1axg/fjwGgyw3kwSe1JIqFh+z9ippTdV0P/03h9sOwaS1njqu7hLd4EyD9r330OXnkkY0KRdP55sXZtRZpqbGxLFjBSxcuJf5z21lNCn03rmTHh9/7PkGTSZRMp6UBC5+b9r33kO3di2KVov2m28YNX48Bw7kcOutX1BWZuKCCzrw7bfTCQur/1T61lvZ7NmTzfL2Y5jFRuJ37OHg40PRGquYdPQYAEtz2gIwecYoJo5z8wIosUOeS31D27YpHD2aT0yX3rD2B0xR0ZhKtOj1Wq6++lKprN8Ali79ga1bdxEfr5fHqRNqX3+c4YvrT2OQnr6db775Ea02nokTJ/LMM58AxYwbN5yJE7sFenh1+Omnn9iwQUyw9+3bnYkTLwzwiIIPV8eoIfow5eWl7I/uTWS79rBjBwN79KD/xIkAaMw93bHt2jFx4kQqKg4AR0lObslE8zIm02E++2wRRUUhlud8jb5dOygoYGTv3ihjx7r3psJC9BWiHH7UjBlCKC6ABMM1X62qrg+Pg+7XX3+dCy64gI4dOzJo0CAAduzYQatWrfjiiy88XR0hISEWIbWzzz6bv/76izfffJNHH30UgIyMDNq0sQoIZGVl1cl+2xIaGkpoaN3+E4PB0CQubE1lnJLmT6c4PZGhlXaqsQAmrd4SdEcbtHSKi2hYb21ZGZXPvYAOeElzAS/OmeTwN2AwQJ8+rXjkkXh6vfYLxvKVGHbuRHvkCPTu7dk277lHCLbpdNCpE3TvDt262f9NSYEHHwRA8+qr6CdO5OTJQiZNWkBOTjlDhrTh++9nEB3tXr/bZZf1ZM+ebL7Nbce9Oh2Jxw4RfTqN8MJ89FWVlEbHsaMgEr1By6Wju8jzQAOR59KG0apVFEeP5lOgFz2H1dGxUCJKo0NCgj/ICWY6dBAJi6SkEHmcOsHZ9ccWn1x/GomOHeMBoUZtMBgsPd3t28cG5fdvq7IfFxcelGMMNK6OUdHXXQpVChGxIuGnq6xEp+5Hc9CqjYlBazBQXCzKk1u0sO7rfv1ErHP4cB46nR6t1g/HeVIS7N2LPj/fZQLCjkxRtUF8PAZz8jUYCOS51N3tehx0t2vXjl27dvHVV1+xc+dOwsPDueWWW7j22mt98mEVRaGyspLOnTvTunVr1qxZYwnuq6qqWLduHa+88kqDtyORNBWER3bjKzar/siO1GNVfOGPbHrvPULzsjlBLNrbbqV375Yul4+MDOH8ywfx04KuXMphmD8fnnvO/Q2mp8OHH4rHNTVw9Kj454zrr4fZs8nKKmX8+C9ITS2iV69EfvzxOo8EZiZP7smLL27k77/yODlgKJ22babbhtVg3n8HWvWCYg0DhrQlQoorSQKMqmCeqYib74pIcXNV2/JKYo875+t77x1KeLiedu2yAjqOYKaxrj+Nha2QmsmkCGcAgtMyDIRtlUpsrBRSc/Z7cnaMhkaZxdDCtGhcCalFie8/P78csN/vnTvHYzBoKS+vJi2tyDJZ51O8UTA/dUr8bYiI2hmK503YQGRkJP/85z8bvPHHH3+cSy65hOTkZIqLi1mwYAG//fYbq1atQqPRcP/99/Piiy/SvXt3unfvzosvvkhERAQzZtQtPZVImiOB9ii19UcutRG39NkYioupeu5FwoBXwsbx7L/da1GZPr0vXy84i0s5jPL112iefdYSvNbLu++C0QgjRsDChXD4sLBEqv23vByGDoUPP6SwqJKLL/6SQ4dy6dAhltWrr6dlS8+sXs49tx1JSZFkZZWS3f98c9C9hrK4FgBs1QtBuAkXdvJovRKJP0hKEjeK21v04ZLp09nT+hyYW2yvXC6xw93zdVJSJA8/PNxvfbKBvm74CtvrT1P/LMnJItuZl1dOWloRNTVCdV2d3Ao2bNXLo6PP7MqW+n5Pjo7RyChxbMaYTNbya2eWYVjVy+PjwyyL6PVaunZtwYEDORw8mOOfoFt1p/Ak6PaFcvkZiltB9/Lly7nkkkswGAwsX77c5bKXXXaZ2xvPzMzkhhtuID09ndjYWPr378+qVassveGPPPII5eXl3HXXXeTn5zN06FBWr14tPbolZwTB4lGq+iOfKChjd5roofNVSZ9xzlzCivM5RAs6PH632zcgF1/cjbti+lNa9D2RR4/C1q1wzjn1v7GsDObNE49nzxZqne3awejR9sspCmRlQYsWlFfD5Mlfsn17BklJkaxZc4Mla+EJWq2GSZO68+mnO1ht6s45QKe/N1HdQlz0fi8Sf6U/tyQYUDNwpwpMMH8+O+f9BayUmW4nBMv5OljG4SvU609TztoDxMaGER0dQnFxFVu3ngZEYBsSEpyChLbBX2xsmIslmzfu/p5qH6Nb2kRyECgqqoRI831NqY29WB3LMDXTbb+ve/VK5MCBHA4cyGH8+K4+/3xeZbrVoNsDtXOJwK2g+4orriAjI4OkpCSuuOIKp8tpNBpqaty3uPnkk09cvq7RaHjmmWd45pln3F6nRNIccNejtHts43gVazUakqNC2A0kR/lomwUF1LzyKgbg7RaX8MqD57v91tBQPROuHMjyz3pyLXvg66/dC7r/9z/Iy6OqXQee2RLJwOq9jB7dqW6wr9FAq1YYjTVMnbqQDRtOEhMTyk8/XU+PHgmefU4bJk/uwaef7uC/G8t4vEcPNIcOYchMR9Fo+PZ0DBoNDB+e7PX6JRJfYSkvN/ee5uaKm0KZ6a5LsJyvg2Ucvkar0dCxGWRb27ePYf/+HEvQ3apVcGa5wb7MOSam6e97b/D092R7jMaaW8+Kiio9zHTbn1979hT3GwcP5nr9OVwiy8sbFVcWiBZMJhNJSUmWx87+eRJwSyQS56SWGF0KyICYaU0tabremWXPv0JYeQl7aMm5r88mIsKzPuZp0/ryNWcBoCxYIPqzXWEywdy5APyn+lxeevUPpk1bTKtW/6Ffv/e4996VLF26n9zcMvPiCjffvIwVKw4THq5nxYoZDBzY2uPPacv48V0JCdFx7Fg+ecPHWJ4vat+VIsLo378VcXFnblZBEjyoAYHae6r+LhITZaa7NsFyvg6WcUgco1ZIWYPu4Oznhtrl5U2nMsKXNOT3pOq9FBdXOQ6662S665aXQ5AG3bK83Gu86umWSCT+pdSo+HS5oCMnB+1bbwLwWacpvHrjII9XMXZsZ25p0Y+8vDBaZGTAunUwZozzN/z4Ixw8SFVENC9ldic6OoTOnePZtSuTvXuz2bs3m3fe+QuNBgYMaE1iYgQ//3wMvV7LkiXXcP75DS/7jooKYcyYzqxadYSfQvqgqlPsixb2YLK0XBIs1M505+TITLczguV8HSzjkDhG7etWg+7WrYM36LYvLz8zg+6G/J7UPviiokpo706mu66QGkDPnqLt7MCBHLfH7RGyvLxRcSvofuutt9xe4X333ef1YCQSiSDS4F7pn7vLBRt5jz1HC2M522jNxI8e8coKw2DQcfnU/iz+oA//ZJsoMXcVdM+ZA8CCqGGUlIXy5Kyh/PvfY8jOLmXduhR+/fU4v/56gv37c9ixIwMQVeZffDGFSy7p7tXndMTkyT1YteoIH+wJZUZsLBQWsrpEWIOMHNnRZ9uRSBqCmoWrnemWPd11CZbzdbCMQ+IYNehWs5rBXF7eokU4Op0GRVHqZF/PFBrye4qpr7zcw0x3WloRpaVVREb6uNRflpc3Km4F3W+88YZbK9NoNDLolkh8QHKUgWiDtl6P0uSoJmgtlZFB5KcfALD87Ot5Zpz34iDTp/fj2Q/O4p9sQ1myBM2770Kog1n5HTvgl18waXU8mdWXiAgDs2YNA6Bly0iuvroPV1/dxzy8En777QSbNp1k1KhOlud9xaWX9uDuu1eycUsGJc8/RdiPy3l7Y1sAn2TTJRJfoGa6CwoqqKyslj3dLgiW83WwjEPimPbtY+z+H8xBd3i4gQ8+mMS2bTvP2PLyhvyeHJaXOxJSqyfTnZAQQWJiBDk5ZRw6lMugQW28/TiOUYPuvDzRoqerR9ivvBxyzaXuMuj2GLeC7uPHj/t7HBKJxIbm5lFqy6l7H6ddTRV/0J5rPnu4QesaObIDh1v35VRGNO0KCmDVKrj88roLmnu5f2kxhNScOGbfMcRpb2rr1lFMn96P6dP7NWhszujQIZYBA1qxc2cmS9uNI+nxy8m95Cu6dImnbdum6czQ1D2Bg5FA79P4+DD0ei3V1Says8tkptsFwXK+DpZx2BLo4ziYqO16Eczl5SZF4cIre5GYmEZqSRWd4vRn3PfWkN+TXaY70mzL5aS8XFEUCgocZ7pBZLtzcso4cCDH90F3glkYVlFE4K0G4c5Qs9wRERDrBwszG5rjuaNBPd2KIvoYNE18J0gkwUhz8ihVMZ1MpeWSLwD4Y9Lt3N83qUHr0+m0XD3tLBa+2ZfZ/AHz59cNutPTRek58HhOf0JCdDz44HkN2m5DmTy5Bzt3ZvL994fo0UP4dDfVfu7m4gkcTATDPtVoNCQlRXL6dDGZmSWWTLcUUnNMsJyvg2UcEBzHcTChlperBKuQmvq9lVZW0RNYfKyYyNDKM/J78/b3pFYHuFNeXlxcZfFtdySk2qtXIps2pfpHTE2vhxYtRMCdnV1/0G0roubH2K+5nju8Cro/+eQT3njjDQ4fPgxA9+7duf/++7n11lt9OjiJ5EynuXiUqhy7+QG6KdVs0HZm+kezfLLO6dP7cc+bZzGbP1CWL0dTUmIp2QLg3XfBaGRPXA/+KmjPHTMHBjyjPHlyT55/fgM//XSE1FRxkWuKQXdz8wQOBoJpn7ZqJYLu06eLLZkYWV7unGA5XwfDOILpOA4WmkJ5ue33ZmtvdCZ/b978nqzl5fVbhqml5QaD1qGLi98VzBMTrUF3fTRCP3dzPne4ZRlmy1NPPcWsWbOYPHkyixYtYtGiRUyePJkHHniAJ5980h9jlEjOaFT/xz4tQukY3bT8VW0p+3M7nX9dCsDRmQ/Suo1vAt+hQ9uR06E3h2iBprwcli2z2WgZzJsHwL8KBqLTaXjkkRE+2W5DOPvstrRuHUVxcRVbtoiLWFPr53bXw9SkSKVkdwm2far2ddsq59buOZTYEyzn60COI9iO42AhOjrUTgk82DLd8ntzjqe/J0+E1Gw9uh1VDqsK5gcPBoGCuZ/twpr7Mehx0D1v3jw++ugjXnrpJS677DIuu+wyXnrpJT788EPef/99f4xRIpE0A1Kn/xMdCj9GDGDaW//02Xo1Gg3Trz2L+WbPbrWUHIAvvoC8PDIiklhGL667rj+dO8f7bNveotVqmDTJqoielBRJjx4JARyR50hPYN8TbPtUDQr27xc3e3Fxos9bInFFsB3HwYRtX7c6qRUsyO/Nd9hZhtUWUquqEv/ALtPtTCXeNtNtMvkh2PQm6PaTXVhzPwY9vnrW1NRw9tln13l+yJAhVFdX+2RQEomkeZH68Tf0PL6VKrSEvPEa4eG+Vc+dPr0f8xHCZ8rq1ZCTAyYTmJ0XXiobjKLR8thj5/t0uw1h8uQelsfnn9+hyWljSE9g3xNs+zQpSdws7tsnbsZkabnEHYLtOA4m1L7uFi3CCQmpRym6kZHfm++wVS83hZuD7vJycV+iZrnBHHRbM92O6NIlHr1eS1mZkVOninw/WE+Cbj+Xlzf3Y9DjoPv6669nnrlc05YPP/yQ6667zieDkkgkzQeluhrjAw8BsLLzRYz953ifb2PAgFbQsxfbaI2muhoWL4Yff4SDBykzRPBfBnHVVX3o1SvR59v2lnHjuhAWJmQ1mmI/t/QE9j3Btk9rZ7qlcrnEHYLtOA4m1L7uYOznlt+b71CDboBSxUY+q6LCGnSHhoLBUG+m22DQ0bWrqNDzS193EJWXN/dj0C0htdmzZ1seazQaPv74Y1avXs2wYcLn9o8//iA1NZUbb7zRP6OUSCRNlr/veYGzS1LJI5yBi9/xyzY0Gg3Tp/fj62fPYjAZQsVcL05v7xkHUkIojz8ePFlugMjIEG6/fQjz5+9hypRegR6Ox0hPYN8TbPtULX8tKqoEpHK5xD2C7TgOJtRMdzDahcnvzXeEhektlotFRi0WBZuyMjsRNaDeTDeIvu6DB3M5cCCHceO6+HawQVRe3tyPQbcy3du3b7f82717N0OGDKFly5YcPXqUo0eP0rJlSwYPHszevXv9PV6JRNKEKMnII/mj1wH4c8LNdBrs44uFDdOm9WWhucSc9evhl1+o0Wh5i3OZOLG77/0tfcDcuReTmfkQHTvG+W0bJkUhpbiKfXmVpBRX+UyARPUwdUVT9ZIPFMG2T2tn4xyVl/vr+JI0XYLtOA4mhg9PBuCcc9oGeCR1CcbvzRfnl0CcozQajbWvu7RaZLVBBN02ImqAS49ulV691L5uP4ipuRt0G42QkSEe+ynTHYzHoC9xK9P966+/+nscEomkGbLl6lmMNRVzQp/IqG9e8+u2evduScKAHqzb2ZFRpACwhD6kEseCJ0b6ddvBir+9LoPJE7i5EEz7tLbQU+2gu7l6qUoaTjAdx8HEuHFdOH16dtApl6vYfm+lldbnm6rPeyDPUTExoeTnV1BcXCXE1CorhZhanUy36/JysFUw92N5eU49AX1GBigKGAz1+3k3gOZ87vDKp1sikUjq4+j63QzftBCA7AefolOs/3vYpk/vx9c7z7IE3f9RhjF6dCfOOy/Z79sONhrL6zIYPIGbG8GyT2sHBrY93c3ZS1XiG4LlOA422vjILtNfqN/biYIydqfB1V2i6RQX0eR83gN9jrKzDYuMhPx8h5lu98rLRabb1r7RZ7ib6VZLy9u2Ba1/XSya67nD46D7wgsvdKmy+8svvzRoQBKJpOmjKApHrr2brhjZG9+Ds1+8p1G2O21aXwY91pe7+It92tb8ZWrP6iDr5W4M3PW67B7rG/9e1cNU4juCYZ+2bGnfw61muhv7+JI0XYLhOJZ4jlajITkqhN1AclRw+ry7Or8EwzkqOtqJV7fTnm5X5eUi052aWkRpaRWRkT78TSWaBWZzckQm29n+8LNyeW2a47nD46mKgQMHMmDAAMu/Pn36UFVVxbZt2zjrrLP8MUaJRNLEWPv6Usaf3gBA7Edvo/HzrKhK587x9Dy3GwO5kxmmKZxzTlvfi440AZq716WkcTAYdLRoYc2+qEJq8viSSCT+whfnl2A4R9llum2D7jqZbrW83HmmOyEhwjLpefhwnm8Hqma6jUYoLHS+nJ+Vy88EPM50v2H2va3NM888Q4mt95xEIjkjKS2pJPzJx9ACu/peSP+rJjTq9qdP78uff4oZ2SeeGNnk/K99QXP3upQ0HklJkeTliZtCtbxcHl8SicRf+OL8EgznKKtXt5Og24NMN4i+7s2bUzlwIIeBA1v7bqBhYWIsJSWixDwuzvFyflYuPxPwWfrp+uuv57///a+vVieRSJooi2f+hxGVh6lET/dFHzT69q+99iwSEyMYObIDkyf3bPTtBwPN3etS0njYiqmpmRZ5fEkkEn/hi/NLMJyjLOrl9ZaX15/pBmtfd8AUzGWmu8H4LOj+/fffCQtzPUsjkUiCk6ysUhYu3MPmzakNWs/BvRmcu+hNAE5OuYnw3t19MTyPaN06itTUB1i79ka02jPzpl/1unRFU/a6lDQetrZhaqZbHl8SicRf+OL8EgznqDpCaiDUy23KyxVFcTvTrfZ1+1XB3FXQ7WFP92uvbeKuu1ZQU+O6zP9MwuPy8iuvvNLu/4qikJ6eztatW3nqqad8NjDJmYtJURqsWOiLdTRnysuNbNx4kjVrjrFmzTF27BDei6GhOlJTH6BlS++UxldP/T/uJZtCQxTdPrG3CGvM7yQs7Mw2ZlC9Lh0pt6o0Za9LZ4hjrAqA1JIqOsXp5bmjgTjKdJ+pxxfI46O5Ir/X4MEX55dgOEdZy8urnGa6S0uNVFeLoNT9THeAgm4PysvzC8r5v/9bi8mkMO7K3lwxtrP8PeFF0B0TE2PXI6nVaunZsyfPPfccEyY0bu+mpPnR1H0Zg5k9e7JYteoIq1cfZcOGk1RUVNu9Hhqqo7Kyhh9/PMKNNw7weP0HdqQybf8iACr/70k08fGW1+R30vg0Z69LR6jHWGllFT2BxceKiQytlOeOBqJmusPD9YSHW7NCZ9rxBfL4aK7I7zX48MX5JdDnKHeE1NTScr1eS2Sk66y71as7B0VRfKtXU1/QbTK5nek+WFDJm1/vx2QS/fIf/3CE9NZx8veEF0H3Z5995odhSCTNw5cxWHnuuXU8/fRvds+1bRvNhPFduKZ7JaNOrEP5+msOEs5/lvfwKuje9fYirqGM3NA4kp6abXlefieBo7l6XdbG9hizLSiU546Go2a6VeVyW86U4wvk8dFckd9r8OKL80sgz1F2Pd0tHGe6bUvL6wuiu3aNR6/XUlpq5NSpYtq3j/HdYNWgO8dJv3hOjlA312igTRunq1F/T/v+OmV5LmVXhvw9mXG7p7usrIy7776bdu3akZSUxIwZM8hx9uVIJB7irqeiSXGuNOmLdTRHPvtshyXgvvjibrzxxkUcWHc1abMVPt32Ly55cjoRH88jsqyQwWRQsmotRmONx9vR/LgSgIyzR4NBzNjK7yTwqF6XfVqE0jG6+fkmy3OHf2nTRtjaOGs5ae7HF8jjo7kiv9fgxxfnl0Cdo5yWlzvIdNdXWg7CwrFLF1FB6HMxtfoy3WppeatWlvu72tj+no79bQ26T+7MQDH/hjz9Pf3992kxadFMcDvofvrpp/nss8+YNGkS06dPZ82aNdx5553+HJvkDKK5+DIGG2vXHuO2274H4MlHhvLjbeHc/8uL9BwzAM1DD8Hu3RAaCtOmoYwZC8CY0j1s2uSZoFp2VgmD07cBkHTLVMvz8juR+Bt57vAv48d3YebMgTz99KhADyVgyOOjeSK/V4k/cVhebiukVivT7Q5qX/eBA/ZBd02NiePH81m16ghvvvkH77+/1TMBs0RRul5v0O2itFz9PVWVGzm1X6xHo9VQkldObqrw//bk92QyKVx00Ze0aPGKRXeoqeN2efnSpUv55JNPmD59OiAswkaMGEFNTQ06nc5vA5ScGTQXX8ZgYt++bK666huqq038e7SJJ/57A7xqc6IeOhRuvhmmTYP4eDTffQe/rOUyDjLvh4OMHt3J7W1t/GQNU8inSqOn5bTLLM/L70Tib+S5w7+Ehxv45JPLAz2MgCKPj+aJ/F4l/iQ62oF6ee3y8qPuZ7pBBN3ffw/ffXeQ06eLOXgwl4MHczl8OJfKSvsKxT17snj77Uvc6/2uL9PtRj+3+js5uSsDU7WJuNZRxLaKImVnBid3ZZDYIc5uufrYvTuT3NxyIiMN9OnT0q33BDtuB92pqamMHDnS8v9zzz0XvV7P6dOnSU5O9svgJGcOzcWXMVjIyChh4sSvKCysZNrgEJ7Y8SqaggLRi3PjjXDTTdC7t/2bxo+nxhBCF2MBB5euh/9c5Pb2iuYvBeBk54F0M3tPgvxOJP5Hnjsk/kYeH80T+b1K/Il9ptsm6LYrL/cs063ahv388zF+/vmY3WshITq6dWtBp05x/PjjYd599y/ato3m8cdHOlqVPe6Wl7sIutXfybG/TwPQaXBbohMiSNmZQcrODAZf2stuufpYu/Y4ABdc0JGQkOaR3HU76K6pqSEkJMT+zXo91dXVTt4hkbiP6qnoqtTLXV/GhqyjOVBaWsXkyfNJSSlkYNcIviz9VATcw4fDr7+KcnJHREZiunAsutU/0u/4Hxw7lm/pH3JFRUU1nfZuBiBkymV2r8nvROJv5LlD4m/k8dE8kd+rxJ9Ye7orIcIc1NYRUssH3A+6r7iiF19/vQeTSaFnzwTzv0R69kygU6c4dDrRNfz221u4775VPPHEL7RuHcXMmYNcr9jdoNuFXZj6ezqxTQTdXYa0IyI2jA1f7CBlZzrg2e9JDbrHju3s1vJNAbeDbkVRuPnmmwm1uWGvqKjgjjvuIDLSKrCydOlS345QEjT408eysX0ZG8uTs7G9P2tqTFx33VK2bj1NYotQNnRZh37NfpHhXrLEecBtxnDVFbD6RyZziBUrDnHvvUPr3ebGFbsZZToBQPvbr7V7LRi8MiXNm+bi6SrxP96ej+Xx0TyR36vEn6jq5eXl1VSHhImAy7an2y7THe7W+SkhIYK1a2+sd9v33juU9PQSXnppI//85/ckJUVy6aU9nL9BDbrLy8UYI2sJZ7pRXq7VaBiZGMr9ezIBkekOixL7IOtoHuXFlUzpn+jW78lorGH9+hQAxo7tUu/yTQW3g+6bbrqpznPXX3+9TwcjCV4aw8eysXwZG8uTMxDenw89tJplyw4SGqpj65U5RH28AkJCYOlSlzYPFi69FIChpPHmkr/cCrpPfPgN4zCREdeO1t271Xk90F6ZkuaP7TFWaiN02pQ8XSX+paHnY3l8NE/k9yrxF2pPN0C5xkA0COstVb3bRkjNGKZn3t58nx6DL7wwhvT0Ej77bAfXXLOItWtvZPhwJ+3AUVEiKVNZKbLdtYNuN8rLAQoO5VBdVUNMYgSJHWLRaDS0aB9DXloRyVlF9Ixznim35c8/T1FSUkViYgT9+7dy6z1NAbeD7k8//dSf45AEMY3pY+lvX8bG+iyB8P58++0tzJ27BYA197eg46v/Ei+8+y4MG+beStq2pbzfQML37CB241pKSv5JVFSI08UVRSF241oAykZPcLrcmeTnKwkM6jF2oqCM3WlwdZdoOsVFNBlPV4n/8NX5WB4fzRP5vUr8QUiIjrAwPRUV1ZQSIoLuTJEFRqOBiAiLZdhRI8TXanNo6P2iRqPhww8vJSurlJUrD3PppfPZuPEWevd2IEqm0Yhsd1qaCLo7dbK+pihulZcDrFt3AoCLxnRiRvdYSo0Kf43swNL5ezi5KwOu7u3y/Sq//CJKyy+8sBNabfP5HbptGSY5MwmEj6W/fBkb67MEYp99//1B7r//JwA+fLA7I+c9Jk6Ud94Jt97q0brCpk4BYGLN/jpCHbXZuT2dC8r2AdDun9NdLnsm+PlKAotWoyHZPEmUHNW0PF0l/sHX52N5fDRP5Pcq8QdqX3eJySwElpcn/kZGglZrCbrDY5z3dDfkftFg0PHNN1czdGg78vLKueiiLzl1qsjxws76ugsLRck51Bt0r19/EoDRozpZfk8TRnUEYPNm961o1X7uMWOaTz83yKBbUg/NyceysT5LY++zNWuOcs01izGZFO69qRe3rvg3FBXB+efD3Lker09zmRBDG89RVi/b7XLZre8vpxWllOnDCR072ovRSyQSif9oTtcwiUTStFD7uotrahUWm11esnLVoNt5Jruh56fIyBB++GEGPXsmkJpaxMUXf2UJ9u1Qg+4cew9wSz93ixZWv3EHGI01lsD6ggs6Wp4/7zxR0v7HH2lUV9fvHV5WZuT330VmvTmJqIEMuiX10Jx8LBvrszTmPvvppyNMnjyfiopqLru0G3PzvkZz4ICYjVy0SPRze8qAAZS3bEME1ZQsW4XiYoZV+WEFAJkDzvNuWxKJROJHmtM1TCKRNC3UTHehsVa4FR0tni8QPd2ugm5o+PkpMTGCVauup02bKPbsyeLyyxdQUVFdeyHxt3am283S8r//TqeszEhCQridr3bfvknExIRSWmpk9+7Mese6ceNJqqpqSE6OoVu3FvUu35SQQbfEJc3Jx7KxPktjbWfVqiNcfvkCKitruPzyniwZdAjt98uFGMbSpdC6tXcr1mgwXHkFAOfn72D79gyHi50+Xcyg9L8BaHH9Vd5tSyKRSPxIc7qGSSSSpoXFq7u6bqZbURSK1KA72nXQ7YvzU6dOcaxadT0xMaFs2HCSJ5/8xX4BZ+Xlboqoqf3cF1zQ0a4PW6vVMHy4eO+mTfWXmK9dK9oax47tgqaZtXnIoFtiwaQopBRXsS+vkpTiKkyKYvHdc0VT8bFsrM/SGNtZufKwJeC+4opeLLohDP2/nxMvzpsH557r9boB9OagezKHWPH9AYfLrP1qM2cjvBdjr72yQdvzJY6O46ZKc/osEkkg8OR8HCy/t2AZR2NwJn1Wn2Gqgczf4MR88ddUE+gRNWn8eQyqCub5VbXOQaE1lJVUYDS3vrjKdPvyHrt//1a89dbFgIMA2FnQ7YZdGMC6dcLiy7a0XEUtMXenr7s5+nOruK1eLmneuLJTaS4+lo3lyenv7axYcYgrr/yGqqoarryyNwsfTEZ/kVk5/J574JZbvFqvHaNGURUaQZvKEo5+8zM8fWGdRfK+XALA6Xa9aNsqOCwdAmHT5i+a02eRSAKFu+fjw4VVQfF7O5N+92fSZ/UZqUvh71lQlmZ9LqI9DHkTkoNn8rup4O9jUM1052XU0sep2EX+woHADHQ6DSERzoNqX99jq6XfqamF9i/Ul+l2UV5eU2Ni40YhojZqVN2ge8QI94Lu/Pxytm0TyZzmJqIGMtMtwWqnUltsRrUrAJjSObpOtiDaoGVK5+gmdXEUnpz+/yz+2s733x9kypSFVFXVcPXVfVjwVC/0ky+FkhIYNw7mzPHF8CE0lJpxIpDvtm8jWVn26r+lpVV03LMJAN3lk32zzQZS33F8sKDSyTuDj+b0WSSSQFPf+RgIit/bmfS7P5M+q89IXQobrrYPuAHKTonnU5cGZlxNlMY4BmNihNZN0f5F9mnOMMjPEUFvfKyWK7vENNo9dnJyLADp6SX2wmYNKC/fsSOD4uIqYmNDHfpqn3tuO7RaDSkphc7V04HffjuBokCvXom0bRvt3gdqQshM9xmOu3Yqd/aNbzY+lo3lyenr7SxbdoCpUxdhNJq45pq+fPnvgRguHCUsKIYOhW+/BYPvyvzDp10JK75jMgf58cfDzJjR1/La2h8PMMZ0BICkm6f6bJve4u5x3D02+K1gmtNnkUiCBWfnY4B5e/Ndvrcxfm9n0u/+TPqsPsNUIzLcOCp9VgAN/H0/tLsctLrGHVsTpLGOwRizenlReQiEAP/f3p3HR1Xd/x9/zWQjCSFhkSUQFhVwYRFwAQRBBURcQW0L1r1K645fq221irWVnztatXWpW1uXVkVxKYqKAqJI2YqiLAISIOyEhCQkk8z9/XEzSYZkMpPkzp07d97Px4MHYeZy59x7zrlzPznnnk9g7bJWsK/ETBPWNm0vfdsk0/vYtrbcY3fsmElKihefz09BQXFNEN6S6eXz55tTy0eM6E5SUv3x3KysNAYO7MTy5dtZtCifiy46tt424O6p5aCR7oTXlHQqbspjadexWPU5s2Z9x4UXmgH3z37Wj38+NIyUM8+AbdugXz/44IOaFBSWmTABv8fLQHbw1euLgt5a+/w7tKGCooy2eIYMsfZzm8FNaYHcdCwiTtLQ9dgp/c0p5bBDIh2rZXYtqD/CHcSA0nxzOwnLrjaY5TWnSheVpUHdAeu6QXdGMexaYN99qddD165tAMjPrzPq3ILp5Y09zx0QyXPdCrrF1ZROxfm++GIzP/nJG1RW+pk8uR9/f2wUyWedCRs2QK9e8OGHZv5Eq7Vvz4GBxwOQOW8OFRXmYi1+v0Hrz+cCcGDk6eCN/WXETe3YTcci4nRO6W9OKYcdEulYLVNWYO12Cc6uNtgmzRxNLz7YUNCdDkDbzIO211tenhl0b9nSQNBdVATl1VPry8rM2ZQQcqTb7zdYsCD089wBgaA71ArmW7cW8f33u/F6PYwe3TPCI4kvsb9blphSOhXne/zxr6ms9DNx4lG8/JdxJJ93DqxaZaYEmzsXcnOj9tmtp1wIwLiD39ZcKP/7322cWroagMOu+EnUPrsp3NSO3XQsIk7nlP7mlHLYIZGO1TLpXazdLsHZ1QbbVA+IFJWlmdPLA1rBvtLqke7MMtvrrVu3wEh3ncXUcnIgqfrRhD17zL8DU8szMyE7u8F9ffvtTvbuLSMzM4XBg0MfR2AxteXLt1NaWn8Gwbx5mwAYPLgLbdumR34wcURBd4JzU0owNzpwoIJ3310DwJ2/PpHkiy6Ar76Ctm3NgPuII6L6+d7zzgNgNJuY9/ZKABa9/Bl92UOlN8mc4u4AbmrHbjoWEadzSn9zSjnskEjHapnDRpqrlBMqCPRARp65nYRlVxvMyu0HhJpeXj3SnZ1se701ONLt9UL79ubPgSnmdRdRCzHdPfA89/DheaSkhF5PoHv3bHJzs6is9LNkydZ677t9ajko6I4L0cwhGEin0hinpQRLpLye7767hrKySvockcOgR24zA+3MTPMZ7n79ol+APn0oyu1FKn7K3n4fgKrZ5t+7jxoCbdpY+nHNrdt4bMehNOVYEqkviESDU64dTilHXeGuL7pe28ibZKYFA+oH3tX/HjJTi6hFyK422CbHDKwbHOkOBN1HnmJ7vdWOdB+yknhgivnaj8088N+ajxK29HluAI/HE/K5bsMw+OSTDYC7g26tXu5wduSxNNOpEBf5MhMtr+err34DGPwz60M8b/wHUlPh7bdh6FDbypB2wXnw55kMKVjGihWnM2z7MgDaTLY2J2hL6zae2nE4kRxLovUFkWhxyrXDKeWA8NdjXa9jIG8SjHwjRJ7umcrT3UR2tMFAnu5iukFGK+Cg+UYr2HfQnHretsdxLf6cpgqsWB400g0QGEf5/FbwAYF1+do2vOicYRg1QXdjz3MHnHxyHm+8sZpFi4IXBVy/fi/5+UWkpiZx8sndIz2MuKOg28ECOQQPFcghOLEXlgbeTk8JZuf5cIJ9+8qYM2c9l7GC41f8x5z68+qrZj5uG6VdNAn+PJOzWMvIZ9bxG8wLbMZF51v2GVbVbTy040g1diyJ1hdEos0p1w4nlCPc9eXEjj6+3nkw5PuJeL22Td4kMy3YrgXm4lvpXcypyRrhbpZot8GsLLMfFJUkw+HnwZLXzTeGPci+NV2A9bRt28qSz2qKBke6898C/7fmz4GXA9kUKz8z3z/kFztr1+5h584S0tKSOOGE0KPhAXVHuv1+A6/XPM+BqeXDhnUjI8O9j5Uo6HaoWOSxDKQrcKJEzOs5a9b3+Hx+rstcDSXAXXfBpBj8JnvYMMoysmlXup9fbPuINKoobN+VnD59LNm91XXr5HbcVA0dSyL2BRE7OOXaEctyRHJ9WdJAwF1Xol6vbeNNgk6jY10K14hmGwyMdBcVlWNkZNQ+GJB3MvsKzQVpY7FoWOCZ7oKCYny+KlKSMGdQZFVvEPidW/XC5bSlwTzwgVHuoUO70apV+JBy0KDOpKcns3dvGWvX7uGoozoAifE8N+iZbsdSHstgTj0fK1du5+23v4/Kvl977Ru6UMTxpevNF664IiqfE1ZyMhVjzQXTbmAxAMZZZ4VcVKOpnFq3TqXzJSLREsn1JdyT27r+iJgCQXdlpZ+qtDrBdVYW+/aZv7yKxUj3YYdlkpLixTCgoOBAbR74wPTywEh3IOhuR4N54AOLqEUytRwgJaV2RPyLL8w0Y36/wbx5gaD78OYeUlxQ0O1QymMZzInn48cfCznllBeZOPF13nxztaX73rHjAJ98spELWY3HMGDYMOgeu+dc2lxspg5Lrr7danvxBZbt24l162Q6XyISLVZdN3T9EYHWrWtH0MuT0+q+wb59ZUBsRrq9Xk9w2rBAnvBQI93tqv+uk0+87vPc4RZRq2v4cDPfd2Axtf/9bwd79pTRunUqJ5wQvRS4TqCg26GUxzKY085HVZWfSy99m6KicgBuvXUuZWXW/Wb/jTdW4/cb/CJzrfnCz35m2b6bwzN+PJVJ5nM25SmtYNQoy/bttLp1Op0vEYkWq64buv6ImMFtIPA+6K0NwI3WrWM60g2HLKYWyBNed6S7Egik8Q4E3XXyiW/aVMiWLUUkJ3sZNiwv4s8NLJQWWEwtsGr5qFE9Gk055gYKuh1KeSyDOe18PPTQIubP/5HWrVPp0qU1mzYV8vDDX1q2/9de+5Y8ChlQssGcxn3hhZbtu1mysmC0GWgnnTEG0qxbpMtpdet0Ol8iEi2RXF/ChdO6/ojUCkwxL/PU9omypFZUVFQBsRnphkMWUwvkga870l2I+SxJEubrh+SBD4xyn3BCbpMWPxs61Bzp/v773ezZU1rzPPdpp7n7eW5Q0O0M/irYudD8eedC8Fcpj+UhnHQ+li8v4Pe/nwfA44+P5+GHxwEwY8bC+ukXmmHz5v0sXLiZn1K9iuQpp0BudKfchM236q+CX55FUffucNU4898WcVLdWilaObTder5EJPYiub6c0LHxkTldf0Rq1Qbd1SPdKSnsKzXXTUhK8pAVo4UEA4upbdlSVJsHvm7QHVi5vB3g9dTLA9/o89z+KtjxmZnre8dnQfeMHTpk0LdvewDemPMDn1Xv59TTelp1aI6l1ctjLf+t6pyLeyDzVfj8LMhoD0Meo2/epLjMY/n997s57LAM2rfPaNb/Lygo5rbbPmb79gPk5LSibVvzT05OKyrSUthUBZ6MVNrnZdM+L9vW81FW5uPii9/C5/MzceJRXH75cQA89dR/WbhwM7ff/jH//GfLVhj/17/MYPuq1uvgAFGfWh4232p1GzUq9jDv8VeZUDIZZj9gXqAtygvqtpyt0c6h7bbzJSLOEcn1pWtmiq4/IhEIBNUHjOqQq87U8pycVnhi9AuqemnD8ibB2Ofgt78w7z13V2/YPhVGvlrvfi/k89w1cc2hueRr7xn7ndCVNWv28P8e/pKyEh+ZOa1Y6E0lo7Dc1dcPBd2xlP8WLLgQc/5GneklpVvN10e+Qd+8SXGVx3LBgh859dSXyM5uxXvvTW7Scx5gLk42ZszfWb9+b/iNgfcWXMGZJ+fZdj5+85uP+e673XTu3Jpnnjmn5mL52GPjOf74Z3jllVVce+3xNc+sNMdrr33DEezhqAObICkJLrBu0bJDhcvHekn623RdNpnG2qiVgXc8tfVQ7Mqh7ZbzJSLOE+76ouuPSGQCI90HquoG3bFbRC0gMNKdn7+/9sUBlwK/MG/5/OcB78CA8+vd523ZUsSGDfvwej3B97tBcU0dde4Z12SdhXG4OdK9abm5MNvhJ3SjpMqw9B7JiRR0x4q/yvxNUIPJNwzAU5MTz+tNios8ln6/wbRpH1JVZbB3bxmnn/4yr79+Ieec0zei/7927R7GjHmZ/PwievbM4e67R1FcXE5h4UH27TsY9Pc33+xk9+5S1ny9lbNG2LOq90cf/cDjj38NwAsvnEeHDhkwdy58/z2Dr7uOq64axHPPLefGG+fw9de/ICmp6U9vrFu3h6VLC7jDs9psBqedBocdZvGRmMLlY/UYVbRZdQsGRgPP8AW30bpTjloi3nO22p1DO97Pl4g4V7jri64/IuEFgu5if3XIFeN0YQGBke6gxyJTUqBtW9i3DzZWmK/l1R88W7DAHOUeNKhzzfFFEtcYS2/mkyNG0mNgl6B3jzyxW83PVt4jOY2C7lgJ5MQLyajNiddptF2lapFXXlnF0qUFZGWlMnx4Hh9++APnn/86Tz99Nr/4xeBG/++qVTsYO/bv7NhRQt++7fn440trLggN+eMf5/P7389jxYrtVh9Gg/bsKeXyy98G4PrrT2D8+CNh9Wo45xwoLwevlz/96XL+/e/VLFtWwIsvruCqqxo/5oa89to3AFyVuTbqU8vD5WPNK/2KLN+2RvYQf2002pqSQ1s3qyIiIu6WlWUGpRvaHWmmfj3/fIeMdJurl2/ffoCKiipSU6sHTw47zAy6ly83/921a73/u2rVToDgFF8RxDWe0nxy9i+iQ4/hpGenUbbfzAB0RJ2g2833SFpILVbq5LqzZLsYKy318dvffgLA7343kvfem8KVVx6H329w9dXvcs89n2GEWEhqyZKtjBr1Ijt2lDBwYCfmz7+i0YAb4LjjOgPYEnQbhsHUqe9RUHCAo47qwP33jwWfDy67zAy4AX77Wzr6Crn7bnOF79/97lP27z/Y5M959dVvOIpd9DqQb/7GceJEqw+nRrg8qpmVOyLbUZy0UTsoh7aIiIgEtGljBo/bjUzYtAn+9CdHjHR36JBBamoShmGupVQjMLtye/X9dbdu9f7vmjV7ADjqqA61L0Z4L5hZuQOv10P3/uZ9fE6XLNodcs/v1nskBd2xkt4l/DZN2S7GHn30S7ZsKaJHj2xuvnkoyclennvuXO6800wvMH365/zyl+9RWRk8Cjh//o+cfvrL7Nt3kKFDuzFv3mV07Nj4yqlQG3R/991uDh6stP6A6nj55ZW8+eZ3JCd7+ec/J5mpEWbMgP/+15yGc9xxUFwMN97IddedSN++7dm5s4R7753fpM/55pudfPfdbqZ4V5svjBtn7j9KwuVRLUnuFNmO4qSN2kE5tEVERCSgZnp5cbmZAhbqjHTHLuj2ej31F1OD+o80Nhh0m6us9enTvvbFCO8FA/eWvavXfDpqRI96i8m59R5JQXesBHLihcx46amXE8+ptm8/wIwZZsqzGTNOp1Ur86kFj8fDvfeexlNPTcDjgWeeWcYFF/yL0lIfAB9+uJ7x4/9BcXEFp57ak48++nnEU226ds2ifft0Kiv9rF69KzoHBmzcuI8bbvgPAH/4w2gGD+4Cy5bBvfeaGzz5JLz0krng2VtvkTrnfWbOHA/AY48trrkwRcKcWm5wRcYa84Uor1oeLh9rfsZQilNyG3yi2xQ/bdQuyqEtIiIiAYGgu6iooua12pHu2E0vh0PShgUcGnQfMr28qspfs9hx3751RrojiGuMjDwKs4cDMPSi/vz8oTMZf9OwoK3cfI+koDtWAjnxgPoNtPrfh+TEa1QjOfGi7a675lFS4uOkk7rys5/1q/f+r351Am+++RPS0pKYPXsNY8a8zAsvLOecc16lrKySs87qzfvvT6l57iUSHo8n6lPMKyv9XHLJLIqLKxgxoju33XYyHDwIl14KlZVw4YVmYDxgANx6q/mfrruO8Sd34uyz+1BZ6WfatA8j+izDMHjttW8ZwA66HdgGaWlw7rlROa6AcPlYDU8SRf0fqW6NFrRRC0Qr97VVlEM7NEvqLobXOadxel+wmmOO1442qHYu4hqBe9uiovKa15wwvRzqpg2rs4J53aDb44EuwSPYmzfvp7y8itS0JEqyWtVejyOIazxDZnJ6daCflOzl2NMOJy0j+NltN98jaSG1WMqbZKZcCuTpDsjoZgYzkaZiiiAnHsDs2WuYMWMhkyf345prhtSMSLfEqlU7+NvfzMUWHnnkjJD5BidOPJqPP76Uc855lS+/3MKXX5plveiiY/jHPybVLuDQBMcd15lPPtkYlaB7zZrdXHbZ2yxevJWsrFRefvl8czXy390N334LHTvCX/5SM1WIu+6Cf/0LNm6EO+/kkUfu4sMP1/Of/6zngw/WMWFC70Y/b8mSbWzYsI8Hkr+DSmDCBGjT+HPtVgiXj7Vrzk8hM6XlbdQC0c59bRXl0K7PkrqL8DqXCOKlL1jFMcdrRxtUOxdxldqR7rpBd+wXUoMQI90d6oxed+5sri9UxydLzXvutt2yeb86W0vN9Tgorjn0GjYT8ibRFxL2HklBd6zlTTJTLhXMh/8Wwaj3ocspkY8eRpATj7xJ7NxZwmWXvU1h4UG++moLDz64iDvvHMkVVwxqVsAL5ujs//3fR/j9BhdddAzDhzeek3vEiO4sXHgF48f/ky1birj88uN47rlzmpVaC2DgQPO5ECuDbr/f4M9/XsxvfvMJBw9Wkp2dxj/+MYlevdrCF1/Agw+aGz77bPCFKSMD/vpXOOMM+POf6f3zn3PzzUN58MFFTJv2IWPGHN7oeQ5MLb8srTrojvLU8rrC5lttaRu1gF25r62iHLa1LKm7CK9ziSDe+kJLOeZ47WiDaucirhP0THc15410h5hefsjz3GsKy3lnsZnVpkP3nJrXg67HgXvGXQvMxdXSu5hTz+vcMybqPZKCbifwJkHHEcAH5t9NmVIeYa7vX/96LoWFBznyyHYcPFjJli1F/PKX73P//V9w992juPjiASQnNy34nTNnPXPnbiA1NYn/9//GRPR/jj22IytX/pJVq3YwcmQPvN4GOphhwGefwQ8/mKuDV1TU/qnz77FGJkkksWLFdvx+o+F9NcHGjfu44op3+PxzM//guHFH8Le/nWtelEpKzNXKDQMuv7zhqd/jxsHFF8M//wnXXMOdnyzk5ZdXsnbtHs4551WmTOnHmWf2rrdQnN9v8Prr33I82+hYstMM4M86q0XH0lRh8602t41awO7c11ZRDluL6q4J1zk722UsxGtfaC7HHK8dbVDtXMSVsqrvA5w50m2mDQsZdNd5njtwPd71YyEAHXrm1NtfzfXYmxQ2lWwi3iMp6I5nEeb6/uztd3n55ZV4PPDPf05iwIBOPPvsUv70pwVs3FjI5Ze/w333LeSee0bzk58cG1HwWlnp59Zb5wJw440ncvjhka+y3a5dOqNG9QxRZAN+8xt44IGw++kMTEyezBvFfdm0qbBJZQj+SINnn13G//3fRxw4UEFmZgoPPzyOa64ZUjtd/rbbzF8C5OXBzJmhd/bII/Cf/8CKFbR5/i888sgZXHzxW3z00Q989NEPeDxwwgldOfvs3px1Vh8GDerMggU/sm1bMb9J+x7KMXN/Z4ZfwT1RKPd1/LKk7iK8ziVCvvhE6wuOOV472qDauYgrNTy93Fkj3SEXUqsz0h24Hu/etM/crEdOvf256fsnGhR0x7MIcuJVVCZx7e3fATB16hBOPNH8rdUNN5zEVVcN5qmnlvD//t9C1q7dw+TJb/KnPy3gd78bwaRJR5OWFrp5PPfcMlav3kX79unccccptW8Yhvlsc14eDB/etOMxDHNBskceMf89fjy0bg2pqebCYqmptX/mz4clS5jQfh9v7DCnmDcn6N66tYirrprNhx/+AMDIkd158cXzg/c1dy489ZT58/PPQ3Z26B127GhOQb/qKrjrLqZ8ewFHL7uGt9/+nvfeW8eyZQV8/fVWvv56K3fd9RldurQmJ6cVHvxcnLzaDLptnFoeD5T7On5ZUneR5oFPgHzxidYXHHO8drRBtXMRV6qdXl539XKnjHSbQfeOHQeoqKgyH4MMEXQHrrO7N5uLrnXo0fA9t1u+f6JBQXc8iyAn3sPvD+O79RV07JjJffedHvReRkYKt946nKlTh/DYY4t56KFFfPPNTqZMeYv27dO55JIBXHXVYPr16xj0/4qKyrnrrnkATJ8+mpycOr+pe/VVc4q1xwN33AHTp5vptMIxDLj5Znj8cfPfTz0Fv/pV6O2feQaWLOHEJPMGZMWK7UyadHT4z6njP/9Zx5Qpb1FYeJC0tCRmzDidm24aGjzSX1gIV15p/nzddTAmgmn0V1wBL78Mn38O117LoA8+YNCgLtxzz6ls21bMBx+s4/331zF37g8UFBygoOAAw9lCu5I95uJp48c36TjcTrmv45cldRdpHvgEyBefaH3BMcdrRxtUOxdxpbrPdPv9BuXllZSXmxkJYj3S3aFDBmlpSZSXV7FtWzE9e+aEnF6emeKhoszH/h0HzP/bwEh3YDtpmFKGxbMwOfE27mzLvW+PBuDhh8eF/I1aVlYad955Chs33sT06aPo2jWLPXvKmDlzMf37/4WhQ5/jueeW1SwCMWPGAnbtKqVv3/ZMnTqkdkelpXD77ebPhgF//KP5nPOOHY0fh99vBrSPP051Qu/GA26AIebnHl64ETCavJiaYRhMnfoehYUHOeGEXJYvn8q0acPqT62/6SbYsgWOPBLuvz+ynXs88PTT5oj8nDnw+us1b+XmZvGLXwxm1qyfsmfPbXz44c+58cYTefh4M+ch550HrWJ7EXYa5b6OX5bUXQS5PxMlX3yi9QXHHK8dbVDtXMSVAinDDANKSipqppZ7vZ4mpcqNBo/HUz9tWKtW5ixTCBrpzmudQulWcxp6Rk4rMnPq36u66fsnGhR0x7M6OfGMQ76o/YaHG146k7KKZE49tScXX9w/7O7atk3n7rtH8+OPN/P++1OYNOlokpO9LF68lauvfpcuXR7m8svf5tFHvwLgwQfHkpJSZxT7oYdgyxYqu/dg65+fwZ+ZCZ9+CoMGmdPBG+L3wy9/WZt+6/nn4eqrwx97v36QkkJ6aRE9KGxy0L1u3V7y84tITU3is88u5+ijDwvewOczny1/+WXweuGll5r2nHXfvuZIP8AN18LKZ+vlW01LS2bcmJ48dnsqQ3/8zHzxJxc16TgSgXJfxy9L6i6C3J9254uPlUTrC445XjvaoNq5iCulpyeTlGT24aKi8pqp5Tk5rVq8ALAVGnyue+xY83HJ446recnr8dCxqBSAw3pk073kC47e/xbdS77AY5j3tm76/omGmAbdM2bM4IQTTiArK4uOHTty/vnns2bNmqBtDMNg+vTp5Obmkp6ezujRo/n2229jVGIHypvE1sGvciAleMrZayuG8/6KPqSkeHnqqbNC5s9uSFKSlwkTevPmmz9hy5ZpPPDAGPr0aU9JiY+XXlpJeXkVp57ak7PP7lP7n7ZuxV89EvzetXfy95Mn8reXPmLvEX2hoABOO81cHM1fZ1Gcqir4xS/M9FterxngXn55ZIVMS4P+5i8ShlBAfn4Re/aURnyMn366EYDhw/PIyDjkt3KbNsEpp9SObN9zT9OfTweY0he6JsPufXDSNXDKqXB+Dvx5GqxdCz++AbN7wpNjYFchZAJlvzLTxkgQM/d1Vr1Rr6wULxN7ZbkqRZLbWFJ3gdyfGV2DX8/olnBplBKtLzjmeO1og2rnIq7j8XiCnut2yiJqAQ2uYP7mm5CfDzk5QdseqB7pHtl2PlM2n89526YyZfP5XPvDYC5J/8R13z9Wi+kz3Z9//jnXXXcdJ5xwApWVldxxxx2MGzeO1atXk1k9qvjAAw/wyCOP8OKLL9KnTx/++Mc/MnbsWNasWUNWVlYsi+8IawrLmVV2Op4jlpFX+hWZlTvYU9GBW/6+ASjhqhuHctRRHcLuJ5ROnVrz61+fzK23DueLL/J57rllfPfdbp58ckJQIL//1tvJLi0l/7gT+X7seQDsObwPL7z8IWf86df0++Df5tTzhQvNUeM2bcxnn//+d/OZ77//HSZPblrhhgyBZcs4PWcvbxXCypU7OO20XhH9108+MYPu007rGfzGm2+avwgoLDQXTHvuObjwwqaVC8zA+evJMNWAh4H9wFpg7QF4dyYwEzKAXkBZ9f85AfBtUz7WEBI1r6MbWFJ3EeT+TBSJ1hccc7x2tEG1cxHXycpKY9++g0Ej3bFeRC0gsJha0Ei3x2M+InmINcuXA3BMp/yg11v7CshaNhkyU3Tv2oiYBt1z5swJ+vcLL7xAx44dWbp0KaeccgqGYTBz5kzuuOMOJk0yK/Gll16iU6dOvPLKK0ydOjUWxXaMujlMDU8SmzNPBuCDR79g/44S2nZtQ5+fDcRvGC2+OfF4PIwY0Z0RI7rXL8fXX5P92j8B+OTWP5mdtZovPZP37n2SXccPY/T9v8Xz7rsweDAMGACzZ0NyMrzyClzUjGnVQ4bAs88yPHUnACtXbo8o6Pb7DebNM4Pu008/3HyxrAz+7//Mae4AQ4eai8L17Nn0ctXNt3oE8ASwDdgIbKj+8yNQCtSdtDEUlI+1cYmY19EtLKm7CHJ/JopE6wuOOV472qDauYir1E0bVljorJHu2me6ixrf0F/FmlXrgcM4qsvuoLc8uneNiKNWL9+/33yIv127dgBs3LiR7du3M27cuJpt0tLSGDVqFIsWLWow6C4vL6e8vDYXXlGR2Yh8Ph8+ny+axW+RQNmaUsb8AxWUlFcEPSNQsG4PX7yyEoDzf30yPo+fTYWl5LWO0s2KYVB1w020Alad/RN2HtUPr7+y3mZLzp1M71NPpOuVl+DZuBE2bcJITqbqlVcwzj/ffIa6iTwDB5IM9CneBBgsW1YQ0flbsWIHe/aU0bp1Kscddxi+//2P5J//HM+qVQBU3Xor/nvugZSUZpWLnQuhdA9Q/VtML9Ct+k9gDZxKA7YYeDb48W7wY7Tx4D82mZpn90p3Q8F86Dii6Z8fJc1poyJ2UzuVeKB2Kk6nNmqdrOpfGO7bV8ru3eZgWXZ2miPObZcu5szizZsLGy2PsWMBa7aaAfrhXQ7go4GR+hjcuzqhnUb62Y4Jug3D4JZbbmHEiBH069cPgO3bzcWxOnXqFLRtp06d+PHHHxvcz4wZM7jnnnvqvf7RRx+RkZFhcamtN3fu3CZt37fOz36/wQv3rMdfZTBsWDbn9SyALQWs2gKrrC1mjdyFCznh66+oTEtj26Tx9N2yOOS2S4GV997LcU89RYdVq1hxww1sT02FDz5o1md7fT7OSk4mo6yI7uxn4cJ1fBDBvt55xxwZ79snjTW/uY3+zzyDp7yc8uxslt58M7sGDTJzc7dE5qvht8kGjm3k/f8WAc07N9HU1DYqEgtqpxIP1E7F6dRGW+7gQXNQceHCJezcaebrLi7eGdE9a7Rt2mSuh7Rhw+5Gy7N3r48DB9PwemHd4Y+zKVRmiRjdu8aynZaWRramlGOC7uuvv57//e9/LFy4sN57hy4CZhhGyIXBfvvb33LLLbfU/LuoqIi8vDzGjRtHmzZtrC20hXw+H3PnzmXs2LGkpES23H7+gQre2FBc8++v3/6O778vITUjhdF3ns2aTuaS/xcenmWOdO/ejee99zBOPx3y8lpe6LIykm+8EYAvr7iRlQPObHTzmnL85CdgGAy24Hk8T79+sGIFQ9jGu1vacfrp40hLa7xZP/20mcLrz9krGPTnvwPgP+00vC+8wAldLMiBunMhfH5Wy/cz6n3HjXQ3tY2K2E3tVOKB2qk4ndqodf7+97dYvvx7evXqi2EUAtsZMKAPEyacGuuisXt3KbfcspbCwspG76E/f/s9AHp22Md5FRdDRYgd2nzv6oR2GphVHY4jgu4bbriB2bNnM3/+fLrVyQnXuXNnwBzx7lInGNq5c2e90e+AtLQ00tLqr56XkpISFxeNppSzZ04ymWnlFPv8FBYU88GfzVHmMb88kTZdcvBjrvDaMycDr2GYC4ItWmSuFD5hAlxzDZx5pvlcdXM88ABs3oyRl8fqK2/E7w29n5pyWL3wzfHHw4oVjGi1i1kH/axbV8igQaEDZ5+vigUL8kmmihO/etN8cfp0vHfeiTfJomdQupwCGe2hdCvmM9oN8CSB4Q/xvsdcrbbLKY58LiZe+pIkNrVTiQdqp+J0aqMtl5NjTsUuLa1i/34zWm3fPsMR57Vz5za0apXMwYOV7Np1kF692ja43Q+7uwL/o2+X3aTUrABcV2zvXWPZTiP93JimDDMMg+uvv5633nqLTz/9lF69ghfB6tWrF507dw6aMlBRUcHnn3/O8OakcHKZQA5T38FK/nHrfyjbX07uUYcx/GcDarapyZn3zDNmwJ2cbKbteu89OPdc6NULpk83UwM0RUEBzJgBgOf++xnd+7BGN49a7r4hQwAYkWEu6hAuX/eSJds4cKCCcW12kVRWCu3bw+9/b66g3lT+KjP39qZXg3Nwh8236oGjbmnkfdyZjzXU+bJ7H+JIfsPgx+IKVu8t58fiCvxGiF9YxZpdbVBtXaIobvqbRIXq3151F1Jz2urlHo8nosXU1qzbB0DfLntIqHtXC8V0pPu6667jlVde4Z133iErK6vmGe7s7GzS09PxeDzcfPPN3HffffTu3ZvevXtz3333kZGRwZQpU2JZdMfok53K0se/YOt3u8jIacXPHxpPUrKXrBQvY7plmjnztm0z03UBPPIInHGGmRv7xRdhyxYzD/W995qj3lOnRjb6fccdUFJirvL9s5/R1+NhYi/4eEsJxb7aXNxB5YiG6qD7mLLNgBE26A7k576s624oAk4/3Rz5b6r8t8wVyku31L6W0c0MtvMm1eZbbXCbmeb7HYY2/r6bhDtfdu1DHGlNYbn9147msKsNqq1LFMVNf5OoUP3bL7CQmhl0O2v1cjBXMF+/fm9w2rBDrF27B4C+IydBxjeJce9qsZgG3X+pTs80evTooNdfeOEFLr/8cgBuu+02ysrKuPbaa9m3bx8nnXQSH330kXJ0V3viia9557Vv8Ho9PPf3iQwc1rV+DtObboKiIjjxRLj2WnNU98EH4Y9/hFmzzFHwefPg/ffNP+3bw7hx5hT0M86Aww4ZxV661AzYAWbOrEkRFpNcqv37Q3Iyrcv2k8d+VqzY0ejmgfzcIyvWmS+MGdP0z8x/y8ylfejU8NKtwTm2w+VbTZR8rJGer2jvQxxpTWE5szYW13u92Odn1sZiJvbCGTeCdrVBtXWJorjpbxIVqv/YcPJIN9Tm6s7P3x9ymzVrzKC7z9AxMPpK99+7RkFMg24jguksHo+H6dOnM3369OgXKM58/vkmpk37EIAHHxzL5LP71N/o3XfhjTfMQPvZZ4OnUaelwc9+Zv5Zu7Z29Hv3bjNH9auvmgH18cebo98TJpg/T5sGhgE//zmcdFLQx9meS7VVK6hZTK2AT1d0CrnQXlmZj0WL8sniIJ03fWO+2NSgu24O7noayFMYLt+q2/OxNvV8RWsf4kh+w+DjLSWNbvPxlhJ6Z6dG95d34djVBtXWJYripr9JVKj+YycQdBcXVzhypLs26G54pLuiooqNG6unl/dt7/571yiJ6TPd0nz5+fu56KJ/U1VlMGVKf6ZNG1p/o+JiuO468+dbb4UBA+pvE9Cnjzn6XVAACxfC734HgwaZwfWSJfCHP5hTydu3hwULID295pnumKueYn6idztFReVs2lTY4GZffJFPRUUVE9vtwlNVBUccYT7T3hS7FgRPqanHgNJ8czux5nzpnLtW/gFf0BTHhhT7/OQfiHEuU7vaoNq6RFHc9DeJCtV/7GRlOXukO/BMd6jp5T/8sJeqKoPWrVPJzdVM4+ZS0B2HDh6s5IIL/sWuXaUcd1xnnn32nIZTqP3+9+YCaYcfDnfdFdnOk5Ph5JPhT3+CZcvM58Gff95c+bxNG9hfPfXkttugzkrzMVUddJ+S2fhiaoHnuad0qp6C3pyp5WUF1m7ndlacL51z1yrxRbZ4T6TbRY1dbVBtXaIobvqbRIXqP3YCI927dpVQVlYJOG2kOxsIPdIdeJ67T5/2IVM2S3iOSBkmkTMMg1/96n2WLNlGu3bpzJr1UzIyGliqfskSePxx8+e//hUyMpr3gV26wBVXmH98PvjySzOQ/+lPm38QVqsOuvv7thBYTG3ixKPrbRZ4nvvEou/MF5oTdKdHmMc70u3czorzpXPuWpkpkX15R7pd1NjVBtXWJYripr9JVKj+YycQdG/ebA5ceTyQne2coDvcSHfN89x92ttWJjfSSHeceeqpJbz44gq8Xg+vv34hPXvm1N/I54Orr6597nrsWGs+PCUFTjkFLr64+bm9o2HAAEhOps3B/XSjqMHF1PbvP8h//7uNzhTTdusP5hXv1FOb/lmHjTRXaayXLiHAAxl55nZizfnSOXetvNYpZKU0/jWUleIlr3WMc5na1QbV1iWK4qa/SVSo/mOn7jPdYAbcXq9zfrkReKZ7584Syssr672/Zo05k7RvXwXdLaGgO44sWPAjN99sLpz2wANjGDPm8IY3nDkTVq6Edu3MFGG4PCdjq1Zw7LEADGFbg9PLP//8R/x+g4s77TRfGDzYfD69qcLm4Mb6PIVh8vVGUrcxq38rzlcsznkCieW1wevxMKZbZqPbjOmWGftFfeq0QeOQNmhY2QbtbuvKBW49B5/TuOlvEhWq/9jJOmSBYSdNLQdo1y6dVq3MwbStW+uvbh8Y6VbQ3TIOGq6UxmzZUsSFF/6byko/kyf345ZbhjW84caNcPfd5s8PPwyHHZYYORmHDIGVKxlCAe9s3s/evWW0a1e7SEXgee4Ls7fCDpo3tTwgkhzcVgmTrzeSuo15/Vtxvuw85wkk5m0DMz3NxF7EvBxh5U1i6+BXabPqFrJ822pePpCSS1H/h+lqVRu0q60rF7j14uCcxk1/k6hQ/cdGYKQ7wEmLqIGZKSovrw3r1u0lP38/hx/eNuj9mhzdfTvEoniuoaA7DhiGwZVXvsPOnSUMHNiJ5547t+GFDAwDfvUrKCszp05fdlni5GQcMgSef56R6bugDFau3M6pp9auTG4+z20wcFczU4Udyo4c22Hy9W4d/Cqzyk6v99/q1i3gjPq34nwlSl5zmzjp2tA3J43e2ankH/BR4jPITPGQ1zrFUSMuawrLmVV2Op4jlpFX+hWZlTsoSe5EfsZQjLIkJhaWW3e+ot3WlQvcenF0TuOhv0n0qP7tF1i9PMBpI91gPte9bt3ees9179tXxq5dpQD07t0uFkVzDQXdceC1175h7twNpKUl8e9/X9Twwmlg5tX+8EMz//bTT+OHxMnJWL2Y2nH+bQQWUwsE3Tt2HOCbb3ZyFLtJ37fTPD8nn9zyz4xmnsIw+XoNPLRZ9X94jliK4Wn4Rnxu/oGwq0zaWv9WnC/lhrSEE/O1ej0eehwyBc8p6p4vw5PE5sz61w/Lz1e02rpygVsvDs+pk/ubRJ/q316pqUmkpSVRXm4+buK0kW4IvYJ5YGp5bm5WvV8eSNPomW6HKyw8yLRp5nPcd9wxkt69QzxPsXUr3Hyz+fPvfw+9eydWTsYBAyApiZzy/XSliJUraxdTmzdvEwCX5e4yXxgxwswz7mRh8vV6MMjybSWv9KuQ2xyoNBKn/qVJEuraYAFXnS/lAreezqmIhFF3irkTR7oDi6nl5+8Pel2LqFlHQbfD/e53n7BjRwl9+7bntttCjM6WlcHEibBrlxl8/vrXQILlZExPr7OYWkHQYmqB57nPTt9svtDSqeV2iDAPb2Zl/ZXam8oV9S9NklDXBgu46nwpF7j1dE5FJAynB921acOCHzurfZ5bQXdLKeh2sMWLt/DXv/4XgL/85SzS0hp4GsAwzPRgS5aYq5XPmgWp5pShhMvJWD3FfAjbWL16FxUV5jSeTz7ZSBJV9N22ytwuHoLuCPPwliR3avFHuab+JWIJd21oIVedL+UCt57OqYiEUXdqtjOnl4ca6VaObqso6Haoyko/U6e+h2HApZcODFoULMhDD8E//wlJSfDvf8PhtWnEEi4nY3XQPTR5Bz6fn9Wrd7FpUyEbNuxjqLeAlLISaNsWBg2KcUEjECZfr4GH4pSu5GcMDbmL1smexKp/iVjCXRtayFXnS7nAradzKiJhxM9Id8PPdGvl8pZT0O1Qjz++mJUrd9CuXToPPTS24Y3+8x+4/Xbz55kz4bTTgt5OuJyM1UH38d4CAoupBaaWX9q1+nnu004zf0HhdGHy9XqAov4Ph1xEDWBsXuvEqn+JWMJdG1rIVedLee+tp3MqTuKQXPF+w+DH4gpW7y3nx+IK/MYhj984pJx2CQ66nTjSbS6ktmtXKQcPVgJQVeVn3TpNL7eKVi93oM2b93PXXfMAeOCBMRx2WAM3e99/Dz/7We308uuua3BfCZWTceBASEqiXUURuRSzYsV2du820xyM9ZjBd1xMLQ8Ik6+3a94kJkaQZzlh6l+aJKGuDRZw1flS3nvr6ZyKEzgkV/yacPcmDimnnZw+0t22bSvS05MpK6tk69YijjiiHfn5RZSXV5GS4qVnz5xYFzHuKeh2oBtv/A8lJT5GjOjOFVc0MBW6sBDOOw+KisyVuJ94AhoZYUmYnIzp6XDMMbBqFUPYxvLl21m7dg+ZlNNj23fmNvEUdEPYfL2R1G3C1L80mdpG07jqfCnvvfV0TiWWHJIrfk1hObM2Ftd7vdjnZ9bGYi5Jf5uuyybHvJx2y6qTos2JI90ej4e8vGzWrt1Dfr4ZdAdWLj/yyHYkJWlydEsp6HaY2bPX8s47a0hO9vLXv56F13vIzVxVFUyeDGvXQl4evPlmzcJpjUmYnIxDhlQH3QXc+8VmqqoMzk3Zitfng5494YgjYl3CpguTrzeSuk2Y+pcmU9toGledL+W9t57OqcSCQ3LF+w2Dj7eUhHzfY1TRZtUtGBgNrIDgzJz2VnH6SDeYz3WvXbun5rluPc9tLf3awkHKyqqYNu0jAG69dRjHHtux/ka/+Q3MmWOO6r7zDnRsYJtEVv1c9wmeAqqqzC+fn3fZab43ZkyjMwJERERE4o5DcsXnH/AFTSk/VF7pV2T5toVcctDNOe2d/kw31F/BXDm6raWg20Fee207+flF9OqVw+9/P6r+Bi+/bK5WDvDii/GxCrfdAkF3Um2e7lMq1pk/xNvUchEREZFwHJIrvsTX0Eh7rczKHZHtyIU57etOL8/OduYaILVBtznSvXbtXkBBt1U0vdwhVq7cwbvvmitsP/nkBDIyDkk9s2QJXHON+fMdd8BPfmJzCePEwIHg9XJYZRFdKKIKL522/2C+d8jq7iIiIiJxzyG54jNTGp9NWJLcKbIduTCnfWCkOzs7zbHPRx+aNqx2pFvTy63gzFpPMFVVfq677j/4/XDBBUdx5pm9gzc4eBAuuQTKy+Hcc+EPf4hNQeNBRoa5mBowhALOSs83Xz/uODjssNiVS0RERCQaHJIrPq91ClkpoUOL/IyhFKfkNvhEt8m9Oe0DQbdTp5ZDbdqw/PwiSkoqaka8+/TRSLcVFHQ7wOLFW1m6tID0dC8PP9xATu5774U1a6BzZ3Naudfl1dbS3I3VU8yHpexgai9zakyzppYnWA7JhKF6FbFV2Hy9Ee0kTL9Vv5ZE5pBc8V6PhzHdGkhzW83wJFHU/5HqEiVWTvsePXIAOPzwtg1v4IBrWN2R7nXrzPvndu3S6dAhw/ayuJGmlzvA8OF5fPnllbzxxifk5mYFv7liBdx/v/nzk09C2xCd1S2syN04ZAi89BK/PSMDVq40X2tq0J2AOSQTgupVxFZh8/VGIly/Vb8WcUyu+L45aUzsRch+3zXnp5CZEvNy2m3IkC7MmXNxw4skO+QaFnime/fuUlauNNdG0vPc1lHQ7RDHHdeJbdtygl+srIQrrzTThF1wAUxy54WohlU5JqtHuj0ff2xOzU9NNfOZ210OcRbVq4itwuXrndiL8IF3uH579K3w3UOh31e/lkTikFzxfXPS6J2dSv4BHyU+g8wUD3mtU/AGMsg4pJx28ng8nHHGkfXfcNC9SU5OKzIyUigt9fHpp5sAPc9tJZfPU45zDz8My5dDTg488USsSxNdYXNMYuZujGS6zXHHmVPwDx40/z18OGSGnu4UtXKIc6heRWwVLl8vmCNhjU41D9tvDfj+kUbeR/1aEk8gV3zPyebfMQpkvR4PPbJSOaZdGj2yUmsD7poNnFHOmHLYvYnH46kZ7f7kkw0A9OnTzpbPTgQKup1q7Vq4+27z50cfNZ/ndjMrc0xmZMDRR9f+uylTyx2S61IspnoVsVW4fL1gjnjnH/CF3iBsvwWMxm5G1a9FxMEceG8SeK5761ZzlpJGuq2joNuJ/H74xS/M1crHjoXLLot1iaLP6hyT1VPMgaYF3Q7JdSkWU72K2Cpcvt6ItrOqP6pfi4gTOfDeJLCCeYCe6baOgm4nevppWLDAnBL9zDNw6JQcN7I6x2Qg6M7ODg7A7S6HOIPqVcRW4fL1RrSdVf1R/VpEnMiB9yaB6eVghh9HHqnp5VZR0O00mzfDbbeZP993H/TsGdPi2MbqHJPnnWdOyf/VryC5CesFOiTXpVhM9Spiq3D5esFczTivdUroDcL2W8CT1Mj76tci4mAOvDcJTC8H6Nkzh7Q0rbltFQXdTmIYJF1/PRw4AMOGwXXXxbpE9rE6x2SPHlBQADNmxLYcCcSSXLzRonoVsVW4fL0AY7pl1l9cKWgn4fqtB466pZH3aVq/dkCeXIkR1X2TOPr7/lBOrlsH3pvUHenW89zW0q8vHKTb/Pl458wxU1w99xwkJVgA4JAck44pRxyxJBdvtKleRWwVLl9vRNeGSPpth6Et79cOyZMrMaC6b5K4+L4PiIe6ddi9Sd2Rbj3PbS0F3U6xaxf9nnvO/Pn3v4djjolteWLFKbkbnVKOOGBJLl67qF5FbBU2X28kwvXblvZrB+XJFZup7pskrr7v46luHXRvUnchNQXd1lLQ7RBJt9xCSnExRv/+eALPdCeqQO7GWHNKORws0ly8vbMbyNEZK6pXEVsF8vW2bCdh+m1z+3XYPLkeM09u1/P0yzm3Ud03SVx938dj3Trk3iQ7O43MzBRKSnz06aOg20p6ptsJFi7E+/rrGF4vVc88Y04vF4kDluTiFRGJFQfmyRWbqO6bJK6+71W3zebxeLj66sGccEIuw4blxbo4rqKRbic4+WQqn3uOdZ9+ypFNSW8lEmOW5OIVEYkVB+bJFZuo7pskrr7vVbct8uij42NdBFdS0O0EHg/GpZeytkMHjox1WUSawJJcvCIiseLAPLliE9V9k8TV973qVhxI08tFpNksycUrIhIrDsyTKzZR3TdJXH3fq27FgRR0i0iz1c3F6zGq6F7yBUfvf4vuJV/gMcxcmGFz8YqIczk5x60VHJgn1xJurzcrNLXu3XROwx2Lvwp2LjR/3rkQ/FVB3/ehOOb73q392gpOacdOKYeNNL1cRFqkb04al6S/TZtVt5Dl21bzenFKLkX9H6Frzk9jWDoRabZ4yHFrBYflyW2xRKk3K0Ra9246p+GOpeb9PZD5Knx+FmS0hyGP0TdvEhN7ER95ut3Wr63glHbslHLYTEG3iLRM/lt0XTYZ45DUHK19BWQtmwyZKa6+iIq4UjzluLWCg/Lktkii1ZsVwtW9m85puGM5+lb47qHq99Prvz/yDfrmTaJ3dir5B3yU+AwyUzzktU5xxgj3odzSr63glHbslHLEgIJuEWm+Orkw60/gcmguTBFpXDzmuLWCQ/LkNlui1psVQtW9m85p2GMBvn+kkfdrj9XrTaJHVpykt433fm0Fp7Rjp5QjRvRMt4g0n3JhiriP+nV8Ur1Zz03nNOyxAEZjz9XG0bFKMKe0Y6eUI0YUdItI8ykXpoj7qF/HJ9Wb9dx0Tq0qYzwcqwRzSjt2SjliREG3iDSfcmGKuI/6dXxSvVnPTefUqjLGw7FKMKe0Y6eUI0b0THc88Fe1fBEIK/bhFG46lngXyIVZupWGn9HxmO8rF6bY4dA0N11O0bWhOazu17pm2yNRr8fRbF9uOqdhjwXwJIHhD/F+HB1rogrVF5zSjp1SjhhR0O10Viyr76al+d10LG4QyIW54ELM3Jd1L6IJngtT7NVImhtdG5rIyn6ta7Z9EvF6HO325aZzGsmxHHVL9erlym0dd8L1BSe0Yzf1p2bQ9HInCyyrf+iiA4Fl9fPfsmcfTuGmY3GTQC7MjK7Br2d0c3XqB3EQXRusZ0W/Vr3YL5Gux3a1Lzed03DHMugB9xxrIomkLzilHTulHDGgkW6nsmJZfTctze+mY3Ej5cKUWNG1IXpa0q9VL7GTCNdju9uXm85puGMJvF8wH/5bBKPe16M6TtaUvuCUduyUcthMQbdTNWVZ/VD5B63Yh1O46VjcSrkwJRZ0bYiu5vZr1Utsuf16HIv25aZzGu5YvEnQcQTwgfm3y4OhuNbUvuCUduyUcthI08udyopl9d20NL+bjkVErKNrgzOpXiSa1L5ETOoLcUNBt1NZsay+m5bmd9OxiIh1dG1wJtWLRJPal4hJfSFuKOh2qsCy+vVWkAzwQEZe48vqW7EPp3DTsYiIdXRtcCbVi0ST2peISX0hbijodqrAsvpAs1M3NHUf/irY8RlsetX821/VvLI3wm8Y/Fhcweq95fxYXIHfCJEr8lBWnI9EFq5ubah7kaiw+tqgvmCNptSLznl8imW9OfD+JiJOKYdb2HU+nVxvbr0/dvI5byYtpOZkgWX1G8y7NzOyZfUj3YcNuVTXFJbz8ZYSin3+mteyUryM6ZZJ35w0645FgoWrW+XRlXgXdG3YU/t6U68N6gvWiuSarXMen5xQbw66v4mIU8rhFnadz3ioN7fdH8fDOW8Gj2FEOtQYn4qKisjOzmb//v20adMm1sUJyefz8cEHHzBhwgRSUlKC3/RXtXxZ/cb2EcjvVy/dQPVvyCzIm7emsJxZG4tDvj+xV1ZkgTdYcz4SRbi6PfpW+O6h0O/XqftG26iIE/ir8BXM54P/FjHh+DakNCXNjQ3XwYQV6pqdwOc8rq+nTqu3GN/fRMQp5WgCR7dRu85nvNWbG+6Pm3jOndBOI401NdIdD6xYVj/UPmzIdek3DD7eUtLoNh9vKaF3dipeT6hnUupIwDQDzRK2boHvH2nkfeXRlTjT3DQ3yikdXQ1ds3XO45MT6y2G9zcRcUo53MKu8xmP9Rbv98fxeM6bQM90J7qm5PdrpvwDvqAp5Q0p9vnJP+Br9mdIA8LWLWA09oxMy+teJC7YcB2UQ+icx6d4qjenlNUp5XALu86n6s1+Lj/nCroTnQ35/Up8kT3BEOl2EiGrcjIqt6O4nfKc2k/nPD7FU705paxOKYdb2HU+VW/2c/k5V9Cd6GzI75eZEsGU8SZsJxGyKiejcjuK2ynPqf10zuNTPNWbU8rqlHK4hV3nU/VmP5efcwXdic6G/H55rVPISmm8qWWleMlr7bCFOuJd2LoFPEmNvK/cjpIglOfUfjrn8Sme6s0pZXVKOdzCrvOperOfy8+5gu5EZ0N+P6/Hw5humY1uM6ZbZmSLqEXKhfn9mixs3XrgqFsaeZ/4zO1oB6e0L6eUIxJOLqvdeU6dfC7sEovcsjrvLRdPOYGd0q/j6ZxFKp5ytDv9c6SWy8+5gm6pze+X0TX49YxulqVD6JuTxsReWfVGvLNSvE1LFxaJ/Ldgdk/45FRYNMX8e3ZP8/VEE65uBz0Q9bp3Hae0L6eUIxLxUFYbroNAfJwLu9h1zkHn3Up21ltLOaVfx9M5C8cJfcmu8+mmeosXLj7nytPtEE7IM2dHfj+/YZB/wEeJzyAzxUNe6xRrR7jjLaeiXcLVbQR174g2GmtOaV9OKUckbC5ri9tpNK+D8VRvdor2d48Dz7srrqfxlBPYKf06js5Zg23UaX3JrvMZR/XmGhGecydcS5WnW5rOhvx+Xo+HHlmp0dm5y/P7tUi4uo333I52cEr7cko5IhFPZQ2IVl+Ix3Nhl2hef3Teoyeevjec0q/j6Zwdyol9ya7zGc/1Fq9ceM41vVzcw+X5/STGnNK+nFKOSMRTWaNN5yI2dN4lmhKpfSXSsYpEgYJucQ+X5/eTGHNK+3JKOawsgxPKGm06F7Gh8y7RlEjtK5GOVSQKFHSLe7g8v5/EmFPal1PKYWUZnFDWaNO5iA2dd4mmRGpfiXSsIlGgoFvcw+X5/STGnNK+nFKOSMRTWaNN5yI2dN4lmhKpfSXSsYpEgYJusVc0czu6PL9fSG7KPeuUY2moHE5pX04pRyScVlY35ZaN5Fic0p9iyWltUNwlkdpXIh2rSBTENOieP38+55xzDrm5uXg8Ht5+++2g9w3DYPr06eTm5pKens7o0aP59ttvY1NYaTk7cju6OL9fg5yQL9MqTjmWxsrhlPbllHJEwilldUL7supcRHIsTjhep3BKGxR3SqT2lUjHKmKxmKYMKykpYeDAgVxxxRVccMEF9d5/4IEHeOSRR3jxxRfp06cPf/zjHxk7dixr1qwhKysrBiWWZguV27F0q/m6lRfrvElmygq351S085xGm1OOJdJyOKF9OaUckYh1WZ3SvqDl5yKSYwHnHK9TxLoNirslUvtKpGMVsVBMg+4zzzyTM888s8H3DMNg5syZ3HHHHUyaZN4cvPTSS3Tq1IlXXnmFqVOn2llUaYlY5HZ0YX6/IE7Ml9lcTjmWppbDCe3LKeWIRKzK6pT2VVdzz0Ukx/Lfm+r8O8Q28XJtsFo89ReJP4nUvhLpWEUsEtOguzEbN25k+/btjBs3rua1tLQ0Ro0axaJFi0IG3eXl5ZSXl9f8u6ioCACfz4fP54tuoVsgUDYnl7HZdi6E0j1AeuhtSndDwXzoOMK2YsW1GJzTqLVRp7QPp5RDWqReO3VTvUZyLGV7qn9wwfG6mKu/88UV1EYlHjihnUb62R7DMBr6dbjtPB4Ps2bN4vzzzwdg0aJFnHzyyWzdupXc3Nya7a655hp+/PFHPvzwwwb3M336dO655556r7/yyitkZGREpewiIiIiIiKSWEpLS5kyZQr79++nTZs2Ibdz7Eh3gMcTvEKiYRj1Xqvrt7/9LbfcckvNv4uKisjLy2PcuHGNnohY8/l8zJ07l7Fjx5KSkhLr4lhr50L4/Kzw2416XyMvkYrBOY1aG3VK+3BKOaRF6rVTN9VrpMcSiXg4Xhdz9Xe+uILaqMQDJ7TTwKzqcBwbdHfu3BmA7du306VLl5rXd+7cSadOnUL+v7S0NNLS0uq9npKSEhcXjXgpZ5N0OQUy2puL+DT4nKHHXPmyyymJ94xhc8XwnFreRp3SPpxSDrFETTt1U71Gcizp1asKl7ngeBOAK7/zxVXURiUexLKdRvq5js3T3atXLzp37szcuXNrXquoqODzzz9n+PDhMSxZAmtuzlfldrSem86pU47F7jzKyqFsD6e0LytEcizHP2b+aWybeDlet/JXmbMWwPw7Fn3fruuPFdfBRLpWJtKxRsKO82HFZ6jeJAIxHek+cOAA69evr/n3xo0bWbFiBe3ataN79+7cfPPN3HffffTu3ZvevXtz3333kZGRwZQpU2JY6gSV/5a5am7pltrXMrqZN4CRpJ4J5HZscB8zEy99jRXcdE6dcixWlSNcf2lpf5KmcUr7skKkx+KW43Wbmr6/BzJfNR8XyGhvb9+36/pjxXUwka6ViXSskbDjfFjxGao3iVBMF1L77LPPOPXUU+u9ftlll/Hiiy9iGAb33HMPTz/9NPv27eOkk07iySefpF+/fhF/RlFREdnZ2WEfbo81n8/HBx98wIQJE5w3jSdUXtjAqElTcr76q5Tb0Wo2nVNb2qhT2kdLyhGuvxx9K3z3UOj3EzGHsoUabadOaV9WiORY3HS8blDn2uAjnQ8yX2VCyWRSOGi+b0fft/L7vCWfE8l1EOwpqxPYVS9NENP7UjvOhxWf4cB6SzROiJ8ijTUds3p5tCjobiF/FczuGfwbvCDVzweeu1E3cy7n2DbqJGH7C+BJAiPU1DP1p5ZSOxVHOuTaEBx0l2FL37fr+9yK62DN2gQJcO/h0PusmF1L7TgfVnyGQ+st0TjhOz/SWNOxz3SLQ+xa0PgXJwaU5pvbiSS6sP2FRm40Qf1JxKWc8F1qVxmsuA6WbWkk4K7exi3XSie0DSex43xY8RmqN2kiBd3SuLICa7cTcTOr+oH6k4i7OOG71K4y2Hn9csO10gltw0nsOB9WfIbqTZpIQbc0Lr1L+G2asp2Im1nVD9SfRNzFCd+ldpXBzuuXG66VTmgbTmLH+bDiM1Rv0kQKuqVxh400n0mpl3omwAMZeeZ2IokubH/BfJZR/UkksTjhu9SuMlhxHUzvZv5JhGulE9qGk9hxPqz4DNWbNJGCbmlcU3PcKlehJLKw/cUDR93SyPsoh7KIGzkhX7xdZbDiOphI+ead0DacxI7zYcVnqN6kiRR0S3iBvLAZXYNfz+gWnA4h/y1zJcdPToVFU8y/Z/c0XxdJFOH6y6AHIutPIuIukX6XuqEMVlwHnXC+7JJIxxoJO86HFZ+hepMmSI51ASRO5E2CrueFzvkaKldh6VbzdV18JJGE6y/h3hcRdwr0/YL58N8iGPU+dDnF3r5v1/XHiutgIl0rE+lYI2HH+bDiM1RvEiEF3RI5bxJ0Gl3/dX8VLL2JegE3VL/mgaU3mxclXYQkUYTqL5G+LyLu5E2CjiOAD8y/Y/G9aNf1x4rrYCJdKxPpWCNhx/mw4jNUbxIBTS+XllOuQhERERERkQYp6JaWU65CERERERGRBinolpZTrkIREREREZEGKeiWllOuQhERERERkQZpITVpuUCuwgUXYgbedRdUU65CkZjyV7V8VdVw+7DjM0TEXuqTIiKWUdAt1gjkKlx6U/CiahndzIBb6cJE7Jf/Vog++VjkfTLcPuz4DBGxl/qkiIilFHSLdZSrUMQ58t+qnn1ySCq/0q3m6yPfCH/zHG4fR98K3z0U3c+IZB8iYh31SRERy+mZbrFWIFdhz8nm3wq4ReznrzJHqQ69aYba15bebG7X7H0Y8P0jUf6MCPYhItZRnxQRiQoF3SIibrNrQfC00HoMKM03t2v2PgCjsRtvKz4jgn2IiHXUJ0VEokJBt4iI25QVtHy7SPdhx2dYVRYRaZz6pIhIVCjoFhFxm/QuLd8u0n3Y8RlWlUVEGqc+KSISFQq6RUTc5rCR5krDgZR99XggI8/crtn7ADxJUf6MCPYhItZRnxQRiQoF3SLiLv4q2PEZbHrV/DsRF/zxJpmpfYD6N8/V/x4ys/GFDsPuwwNH3RLlz4hgHyJiHfVJsYq+i0WCKOgWEffIfwtm94RPToVFU8y/Z/c0X080eZPM1D4ZXYNfz+gWecqfcPsY9ED0P0OpiUTspT4pLaXvYpF6lKdbRNxBuWXry5sEXc8zVxouKzCfwzxsZNNGqcLtw47PEBF7qU9Kc+m7WKRBCrpFJP6FzS3rMXPLdj0v8W4avUnQaXR092HHZ4iIvdQnpan0XSwSkqaXi0j8U25ZERGR2NJ3sUhICrpFJP4pt6yIiEhs6btYJCQF3SIS/5RbVkREJLb0XSwSkoJuEYl/yi0rIiISW/ouFglJQbckJuWPdBfllm0+9QWR+KI+K06l72KRkLR6uSSe/LfM1TXrLvaR0c38olAai/gVyC3bYN3OVN02RH1BJL6oz4rT6btYpEEKuiWxKH+kuym3bOTUF0Tii/qsxAt9F4vUo6BbEofyRyYG5ZYNT31BJL6oz0q80XexSBA90y2JQ/kjRUzqCyLxRX1WRCSuKeiWxKH8kSIm9QWR+KI+KyIS1xR0S+JQ/kgRk/qCSHxRnxURiWsKuiVxKH+kiEl9QSS+qM+KiMQ1Bd2SOJQ/UuJNtPLxqi+IxBf1WRGRuKagWxJLIH9kRtfg1zO6Kd2KOEv+WzC7J3xyKiyaYv49u6f5uhXUF0Tii/qsiEjcUsowSTzKHylOZ1c+XvUFkfiiPisiEpcUdEtiUv5IcSq78/GqL4jEF/VZEZG4o+nlIiJOony8IiIiIq6ioFtExEmUj1dERETEVRR0i4g4ifLxioiIiLiKgm4RESdRPl4RERERV1HQLSLiJMrHKyIiIuIqCrpFRJxG+XhFREREXEMpw0REnEj5eEVERERcQUG3iIhTKR+viIiISNzT9HIRERERERGRKFHQLSIiIiIiIhIlCrpFREREREREokRBt4iIiIiIiEiUKOgWERERERERiRIF3SIiIiIiIiJRoqBbREREREREJEoUdIuIiIiIiIhEiYJuERERERERkShR0C0iIiIiIiISJQq6RURERERERKJEQbeIiIiIiIhIlCjoFhEREREREYkSBd0iIiIiIiIiUaKgW0RERERERCRKkmNdgGgzDAOAoqKiGJekcT6fj9LSUoqKikhJSYl1cUTqURuVeKB2KvFA7VScTm1U4oET2mkgxgzEnKG4PuguLi4GIC8vL8YlEREREREREbcpLi4mOzs75PseI1xYHuf8fj/btm0jKysLj8cT6+KEVFRURF5eHvn5+bRp0ybWxRGpR21U4oHaqcQDtVNxOrVRiQdOaKeGYVBcXExubi5eb+gnt10/0u31eunWrVusixGxNm3a6OImjqY2KvFA7VTigdqpOJ3aqMSDWLfTxka4A7SQmoiIiIiIiEiUKOgWERERERERiRIF3Q6RlpbG3XffTVpaWqyLItIgtVGJB2qnEg/UTsXp1EYlHsRTO3X9QmoiIiIiIiIisaKRbhEREREREZEoUdAtIiIiIiIiEiUKukVERERERESiREG3hebPn88555xDbm4uHo+Ht99+O+h9j8fT4J8HH3ywZpvy8nJuuOEGOnToQGZmJueeey5btmwJ2s++ffu45JJLyM7OJjs7m0suuYTCwkIbjlDiXUvb6N69e7nhhhvo27cvGRkZdO/enRtvvJH9+/cH7UdtVFrCimtpgGEYnHnmmQ3uR+1UmsuqNvrll19y2mmnkZmZSU5ODqNHj6asrKzmfbVRaQkr2un27du55JJL6Ny5M5mZmQwePJg33ngjaD9qp9Jc4drogQMHuP766+nWrRvp6ekcffTR/OUvfwnaJl5iJwXdFiopKWHgwIE88cQTDb5fUFAQ9Of555/H4/FwwQUX1Gxz8803M2vWLF577TUWLlzIgQMHOPvss6mqqqrZZsqUKaxYsYI5c+YwZ84cVqxYwSWXXBL145P419I2um3bNrZt28ZDDz3EqlWrePHFF5kzZw5XXXVV0H7URqUlrLiWBsycOROPx9PgftROpbmsaKNffvkl48ePZ9y4cXz99dcsWbKE66+/Hq+39tZMbVRawop2eskll7BmzRpmz57NqlWrmDRpEj/96U9Zvnx5zTZqp9Jc4drotGnTmDNnDv/4xz/47rvvmDZtGjfccAPvvPNOzTZxEzsZEhWAMWvWrEa3Oe+884zTTjut5t+FhYVGSkqK8dprr9W8tnXrVsPr9Rpz5swxDMMwVq9ebQDGV199VbPNl19+aQDG999/b+1BiKs1p4025F//+peRmppq+Hw+wzDURsVaLWmnK1asMLp162YUFBTU24/aqViluW30pJNOMu68886Q/0dtVKzU3HaamZlpvPzyy0GvtWvXznjuuecMw1A7Fes01EaPPfZY4w9/+EPQa4MHD665dsZT7KSR7hjZsWMH77//ftAI4dKlS/H5fIwbN67mtdzcXPr168eiRYsA8zfj2dnZnHTSSTXbDB06lOzs7JptRKzQUBttyP79+2nTpg3JycmA2qjYK1Q7LS0tZfLkyTzxxBN07ty53v9TOxW7NNRGd+7cyeLFi+nYsSPDhw+nU6dOjBo1ioULF9ZsozYqdgp1LR0xYgSvv/46e/fuxe/389prr1FeXs7o0aMBtVOJrhEjRjB79my2bt2KYRjMmzePtWvXcsYZZwDxFTsp6I6Rl156iaysLCZNmlTz2vbt20lNTaVt27ZB23bq1Int27fXbNOxY8d6++vYsWPNNiJWaKiNHmrPnj3ce++9TJ06teY1tVGxU6h2Om3aNIYPH855553X4P9TOxW7NNRGN2zYAMD06dO5+uqrmTNnDoMHD+b0009n3bp1gNqo2CvUtfT111+nsrKS9u3bk5aWxtSpU5k1axZHHHEEoHYq0fX4449zzDHH0K1bN1JTUxk/fjxPPfUUI0aMAOIrdkq27ZMkyPPPP8/FF19Mq1atwm5rGEbQM4kNPZ946DYiLRWujRYVFXHWWWdxzDHHcPfddwe9pzYqdmmonc6ePZtPP/006JnDhqidih0aaqN+vx+AqVOncsUVVwAwaNAgPvnkE55//nlmzJgBqI2KfUJ95995553s27ePjz/+mA4dOvD2229z0UUXsWDBAvr37w+onUr0PP7443z11VfMnj2bHj16MH/+fK699lq6dOnCmDFjQv4/J8ZOCrpjYMGCBaxZs4bXX3896PXOnTtTUVHBvn37gn5js3PnToYPH16zzY4dO+rtc9euXXTq1Cm6BZeEEaqNBhQXFzN+/Hhat27NrFmzSElJqXlPbVTsEqqdfvrpp/zwww/k5OQEvX7BBRcwcuRIPvvsM7VTsUWoNtqlSxcAjjnmmKDXjz76aDZv3gzoWir2CdVOf/jhB5544gm++eYbjj32WAAGDhzIggULePLJJ/nrX/+qdipRU1ZWxu9+9ztmzZrFWWedBcCAAQNYsWIFDz30EGPGjImr2EnTy2Pgb3/7G0OGDGHgwIFBrw8ZMoSUlBTmzp1b81pBQQHffPNNTcMZNmwY+/fv5+uvv67ZZvHixezfv79mG5GWCtVGwRzhHjduHKmpqcyePbveb8XVRsUuodrpb37zG/73v/+xYsWKmj8Ajz76KC+88AKgdir2CNVGe/bsSW5uLmvWrAl6fe3atfTo0QNQGxX7hGqnpaWlAEEr6gMkJSXVzNZQO5Vo8fl8+Hy+RttfXMVOti3ZlgCKi4uN5cuXG8uXLzcA45FHHjGWL19u/PjjjzXb7N+/38jIyDD+8pe/NLiPX/7yl0a3bt2Mjz/+2Fi2bJlx2mmnGQMHDjQqKytrthk/frwxYMAA48svvzS+/PJLo3///sbZZ58d9eOT+NfSNlpUVGScdNJJRv/+/Y3169cbBQUFNX/URsUqVlxLD0UDq6KqnUpzWdFGH330UaNNmzbGv//9b2PdunXGnXfeabRq1cpYv359zTZqo9ISLW2nFRUVxpFHHmmMHDnSWLx4sbF+/XrjoYceMjwej/H+++/XbKd2Ks0Vro2OGjXKOPbYY4158+YZGzZsMF544QWjVatWxlNPPVWzj3iJnRR0W2jevHkGUO/PZZddVrPN008/baSnpxuFhYUN7qOsrMy4/vrrjXbt2hnp6enG2WefbWzevDlomz179hgXX3yxkZWVZWRlZRkXX3yxsW/fvigembhFS9toqP8PGBs3bqzZTm1UWsKKa+mhGgq61U6luaxqozNmzDC6detmZGRkGMOGDTMWLFgQ9L7aqLSEFe107dq1xqRJk4yOHTsaGRkZxoABA+qlEFM7leYK10YLCgqMyy+/3MjNzTVatWpl9O3b13j44YcNv99fs494iZ08hmEY0RlDFxEREREREUlseqZbREREREREJEoUdIuIiIiIiIhEiYJuERERERERkShR0C0iIiIiIiISJQq6RURERERERKJEQbeIiIiIiIhIlCjoFhEREREREYkSBd0iIiIiIiIiUaKgW0RERERERCRKFHSLiIi4kGEYjBkzhjPOOKPee0899RTZ2dls3rw5BiUTERFJLAq6RUREXMjj8fDCCy+wePFinn766ZrXN27cyO23385jjz1G9+7dLf1Mn89n6f5ERETcQEG3iIiIS+Xl5fHYY49x6623snHjRgzD4KqrruL000/nxBNPZMKECbRu3ZpOnTpxySWXsHv37pr/O2fOHEaMGEFOTg7t27fn7LPP5ocffqh5f9OmTXg8Hv71r38xevRoWrVqxT/+8Y9YHKaIiIijeQzDMGJdCBEREYme888/n8LCQi644ALuvfdelixZwvHHH8/VV1/NpZdeSllZGbfffjuVlZV8+umnALz55pt4PB769+9PSUkJd911F5s2bWLFihV4vV42bdpEr1696NmzJw8//DCDBg0iLS2N3NzcGB+tiIiIsyjoFhERcbmdO3fSr18/9uzZwxtvvMHy5ctZvHgxH374Yc02W7ZsIS8vjzVr1tCnT596+9i1axcdO3Zk1apV9OvXrybonjlzJjfddJOdhyMiIhJXNL1cRETE5Tp27Mg111zD0UcfzcSJE1m6dCnz5s2jdevWNX+OOuoogJop5D/88ANTpkzh8MMPp02bNvTq1Qug3uJrxx9/vL0HIyIiEmeSY10AERERib7k5GSSk82vfb/fzznnnMP9999fb7suXboAcM4555CXl8ezzz5Lbm4ufr+ffv36UVFREbR9ZmZm9AsvIiISxxR0i4iIJJjBgwfz5ptv0rNnz5pAvK49e/bw3Xff8fTTTzNy5EgAFi5caHcxRUREXEHTy0VERBLMddddx969e5k8eTJff/01GzZs4KOPPuLKK6+kqqqKtm3b0r59e5555hnWr1/Pp59+yi233BLrYouIiMQlBd0iIiIJJjc3ly+++IKqqirOOOMM+vXrx0033UR2djZerxev18trr73G0qVL6devH9OmTePBBx+MdbFFRETiklYvFxEREREREYkSjXSLiIiIiIiIRImCbhEREREREZEoUdAtIiIiIiIiEiUKukVERERERESiREG3iIiIiIiISJQo6BYRERERERGJEgXdIiIiIiIiIlGioFtEREREREQkShR0i4iIiIiIiESJgm4RERERERGRKFHQLSIiIiIiIhIlCrpFREREREREouT/Az+0bnY6SZYYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a single figure\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Plot actual data points (scatter) and predicted data (line) for frequent authors\n",
    "plt.scatter(eighteenth_frequent[\"publication_year\"], eighteenth_frequent[\"estc_id\"], label=\"Frequent Authors (Actual)\", color=\"skyblue\", marker='o')\n",
    "plt.plot(eighteenth_frequent[\"publication_year\"], predicted_counts_freq, label=\"Frequent Authors (Predicted)\", color=\"navy\")\n",
    "\n",
    "# Plot actual data points (scatter) and predicted data (line) for other authors\n",
    "plt.scatter(eighteenth_other[\"publication_year\"], eighteenth_other[\"estc_id\"], label=\"Other Authors (Actual)\", color=\"orange\", marker='o')\n",
    "plt.plot(eighteenth_other[\"publication_year\"], predicted_counts_other, label=\"Other Authors (Predicted)\", color=\"red\")\n",
    "\n",
    "# Set labels and title\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Publication Count\")\n",
    "plt.title(\"Predicted vs Actual Counts for Frequent and Other Authors\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ed0d99e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here I simply look at the regression line\n",
    "slope_freq, intercept_freq, r_freq, p_freq, stderr_freq = scipy.stats.linregress(pd.concat([seventeeth_frequent[\"publication_year\"], eighteenth_frequent[\"publication_year\"]]), pd.concat([seventeeth_frequent[\"estc_id\"], eighteenth_frequent[\"estc_id\"]]))\n",
    "r_freq, p_freq=scipy.stats.mstats.pearsonr(pd.concat([seventeeth_frequent[\"publication_year\"], eighteenth_frequent[\"publication_year\"]]), pd.concat([seventeeth_frequent[\"estc_id\"], eighteenth_frequent[\"estc_id\"]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1904d5b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression line: y=-236.40+0.15x, r=0.78\n"
     ]
    }
   ],
   "source": [
    "line_freq = f'Regression line: y={intercept_freq:.2f}+{slope_freq:.2f}x, r={r_freq:.2f}'\n",
    "print(line_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3aa6456",
   "metadata": {},
   "outputs": [],
   "source": [
    "slope_other, intercept_other, r_other, p_other, stderr_other = scipy.stats.linregress(pd.concat([seventeeth_other[\"publication_year\"], eighteenth_other[\"publication_year\"]]), pd.concat([seventeeth_other[\"estc_id\"], eighteenth_other[\"estc_id\"]]))\n",
    "r_pears_other, p_pears_other=scipy.stats.mstats.pearsonr(pd.concat([seventeeth_other[\"publication_year\"], eighteenth_other[\"publication_year\"]]), pd.concat([seventeeth_other[\"estc_id\"], eighteenth_other[\"estc_id\"]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff406d7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression line: y=-80.67+0.05x, r=0.47\n"
     ]
    }
   ],
   "source": [
    "line_other = f'Regression line: y={intercept_other:.2f}+{slope_other:.2f}x, r={r_other:.2f}'\n",
    "print(line_other)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "346ea3c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(pd.concat([seventeeth_frequent[\"publication_year\"], eighteenth_frequent[\"publication_year\"]]),\n",
    "         pd.concat([seventeeth_frequent[\"estc_id\"], eighteenth_frequent[\"estc_id\"]]),\n",
    "         linewidth=1, marker='o', label='Data points (frequent authors)', color='skyblue')\n",
    "plt.scatter(pd.concat([seventeeth_other[\"publication_year\"], eighteenth_other[\"publication_year\"]]),\n",
    "         pd.concat([seventeeth_other[\"estc_id\"], eighteenth_other[\"estc_id\"]]),\n",
    "         linewidth=1, marker='o', label='Data points (other authors)', color='orange')\n",
    "\n",
    "plt.plot(pd.concat([seventeeth_frequent[\"publication_year\"], eighteenth_frequent[\"publication_year\"]]),\n",
    "         intercept_freq + slope_freq * pd.concat([seventeeth_frequent[\"publication_year\"], eighteenth_frequent[\"publication_year\"]]),\n",
    "         label='Regression line (frequent authors)', color='navy')\n",
    "plt.plot(pd.concat([seventeeth_other[\"publication_year\"], eighteenth_other[\"publication_year\"]]),\n",
    "         intercept_other + slope_other * pd.concat([seventeeth_other[\"publication_year\"], eighteenth_other[\"publication_year\"]]),\n",
    "         label='Regression line (other authors)', color='red')\n",
    "\n",
    "plt.xlabel('Publication Year')\n",
    "plt.ylabel('Number of Publications')\n",
    "plt.legend(facecolor='white')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fb44c7a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
